Reading additional input from stdin... OpenAI Codex v0.124.0 (research preview) -------- workdir: /opt/MM_D-ND model: gpt-5.5 provider: openai approval: never sandbox: workspace-write [workdir, /tmp, $TMPDIR, /root/.codex-lab/memories] reasoning effort: none reasoning summaries: none session id: 019e01d1-532c-7303-a55f-086eabdc6590 -------- user Sei l'AI-Lab D-ND. Leggi tools/data/agent_field_live.md per il campo vivo (tensioni, ultimi risultati, convergenze, osservazioni). Leggi tools/LAB_AGENT_CONTEXT.md per modello, regole, errori da evitare. Esegui un esperimento e scrivi il report in tools/data/reports/agent_20260507_0942.md warning: Codex could not find bubblewrap on PATH. Install bubblewrap with your OS package manager. See the sandbox prerequisites: https://developers.openai.com/codex/concepts/sandboxing#prerequisites. Codex will use the vendored bubblewrap in the meantime. codex Leggo prima il campo vivo e il contesto operativo, poi scelgo l’esperimento in base alla direttiva o alla tensione più vincolante. Verifico anche l’ambiente API come richiesto dalle regole permanenti. exec /bin/bash -lc "sed -n '1,260p' tools/LAB_AGENT_CONTEXT.md" in /opt/MM_D-ND succeeded in 0ms: # AI-Lab D-ND — Contesto Operativo > Questo file viene iniettato nel prompt dell'agente ad ogni ciclo. > Contiene tutto ciò che serve per operare con consapevolezza. ## Chi sei Sei l'AI-Lab del sistema D-ND. Giri autonomamente ogni notte come istanza Claude Code. Non sei una pipeline di script — sei un ricercatore che pensa, esplora, scrive codice, lo esegue, valuta i risultati, e aggiorna lo stato del sistema. Il tuo lavoro produce risultati che vanno sul sito d-nd.com e alimentano il sistema THIA. Quello che trovi conta — non per te, per il sistema e per chi lo legge. ## Il modello D-ND — nucleo La regola: f(x) = 1 + 1/x. M = [[1,1],[1,0]]. det(M) = -1. - Il punto fisso è φ = (1+√5)/2. Al punto fisso, addizione e moltiplicazione coincidono. - L'attrattore è stabile: |f'(φ)| = 1/φ² < 1. Ogni iterata converge. - Il rinforzo è impossibile — proprietà analitica, non empirica. - det = -1: area preservata, orientamento invertito. Incompletezza come generazione. - g(x) = 1/(1+x): la Fermi-Dirac con punto fisso 1/φ. Versione probabilistica di f. ## Il condensato — cosa è stato verificato ASSIOMI (scelte fondative, accettate): - A1: f(x)=1+1/x, M=[[1,1],[1,0]], det=-1 - A2: det=-1 è la necessità strutturale del confine - A3: Al punto fisso, R+1=R (addizione = moltiplicazione) - A4: Il modus — la qualità della domanda determina la qualità dell'inversione - A5: Il sistema è autopoietico — ogni ciclo produce R+1 dalla base R - A9: Il terzo incluso — tra A e non-A c'è lo zero - A11: La combo — tre o più enti simultanei, risultante non sommabile - A14: Cascata — ciò che si scopre vive nel seme, non nel nodo FATTI (dimostrati/verificati): - F1: Residuo Cassini = (-1)^(n+1)/F(n)², decade come 1/φ^(2n) - F2: Cammino gap primi su Z/6Z confinato a {2,4}. Zero violazioni su 567K coppie. - F3: Il rinforzo è impossibile. Classificazione binaria: MOLLA (r≠φ) o ZERO (r=φ). - F4: Separazione di scala — M opera a scala locale, modulazione zeta non si propaga. - F5: Frame diagnostica universale — firma (dipolo, LVL-2, convergenza) su 18 domini. - F6: La firma dello zero — CV dei gap tra phi-crossing converge a φ-1 nel regime caotico. CLAIM (falsificabili, sotto test): - C1: I primi sono l'unico dominio dinamico sotto M (tra 7 testati). - C2: La coincidenza numerica non è mai prova. Principio metodologico. - C3: Il linguaggio deterministico — un termine nomina una funzione reale, o è superfluo. ## Strutture trovate dal lab (sessioni interattive) - Tetraedro TQGE: 4 vertici (T,Q,G,E), 6 lati con perno i, 5 ponti, 1 vuoto (QxG) - Tetraedro orientato: T termico, Q chirale, E fase, G passivo - R è il frame (5° vertice): connesso a tutti ma senza perno i - Tre specie perno i: Wick (continuo tempo), fase (continuo gauge), discreto (primi) - Operatore Q→G: e^{iH·ln(p)/ℏ} — evoluzione in tempo logaritmico - Metrica primi: g_n = p_n/2, curvatura GUE r=0.503 z=22.5 vs shuffle - Tensore metrico: g_n = (p_n/2)², de Sitter 1+1D con a(t)=e^t/2 - α catena: α^n·a₀ mappa scale fisiche, deserto 3-10, residuo pentagonale 72.5° - g(x)=1/(1+x) = Fermi-Dirac, punto fisso 1/φ. f→g = ponte TxQ algebrico. ## Le 10 domande fondamentali (incrocio teorie) | Coppia | Domanda | Ponte | |--------|---------|-------| | ExR | Come coesistono statico e radiante? | onda EM | | GxE | Come coesistono neutro-curvo e carico-piatto? | buco nero carico | | GxR | Come coesistono piatto e singolare? | orizzonte eventi | | QxE | Come coesistono libero e legato? | atomo di idrogeno | | **QxG** | **Come coesistono continuo e discreto?** | **VUOTO** | | QxR | Come coesistono non-relativistico e relativistico? | eq. Dirac | | TxE | Come coesistono freddo e plasma? | funzione partizione | | TxG | Come coesistono piatto e radiante? | temperatura Hawking | | TxQ | Come coesistono vuoto e pieno? | matrice densità | | TxR | Come coesistono 0K e c? | gas relativistico | QxG è il vuoto — l'unico lato senza ponte. Il vuoto non è assenza del ponte — è dove i due lati del dipolo sono lo stesso. Wheeler-DeWitt: Ĥ|Ψ⟩ = 0, niente tempo. ## Vincoli operativi - La prima impressione contiene il segnale. Non elaborare — osservare. - Una risultante, non una lista. Se ci sono più possibilità, non hai tagliato. - Formule dove servono. Fenomeni reali. Niente filosofia. Niente metafore. - Se non sai, lascia vuoto. Blank > Wrong. Errore costa 3x di un non-so. - Ogni claim va testato col suo opposto. Se l'opposto è altrettanto coerente, la tensione è il contenuto. - Le coincidenze numeriche non sono mai prova (C2). - Le dissonanze sono il segnale, non il rumore. L'errore è il varco. - La via più breve verso la risultante. Principio di minima azione. - **La struttura contiene già la risposta.** Un dipolo sa se è aperto o chiuso. Un'assonanza sa se risuona o no. Una porta sa dove sei entrato. Se interponi un numero tra la struttura e la decisione, stai aggiungendo (det=+1) — il numero decide al posto della struttura. I numeri misurano i dati. Le strutture decidono il sistema. Non mischiare i due. - **Perimetro come parte atomica del claim.** Universal claims ("X holds for all", "Y is stable across", "exactly zero", "always", "80% of", "N% explained by") devono dichiarare il perimetro come parte atomica del claim, non come nota a margine. Esempio corretto: "self-transition mod-3 = 0 esattamente per p > 5" (perimetro p>5 atomico). Esempio falsificabile: "self-transition mod-3 is exactly zero" + nota separata sull'eccezione. Se la tabella nel report mostra eccezioni nel perimetro, il claim è falsificato — anche se la maggioranza conferma. **Cinque cycle consecutivi (2026-04-30 19:05/19:19/19:46 + 2026-04-30 03:30 + 2026-05-01 03:30) hanno avuto HIGH flag su questo pattern.** Riformulare prima di scrivere — non aspettare il falsifier. ## Come operare — il modus Non seguire passi. Segui il modus: **espandi → osserva → taglia → risultante**. ### 1. Espandi Leggi il seme, le tensioni, il contesto. Non scegliere subito — lascia che il campo si carichi. Guarda dove più tensioni convergono sullo stesso punto. Se METRIC_TENSOR e BOUNDARY e BRODY_CROSSOVER parlano tutte della stessa cosa da angoli diversi, il punto è lì — non in una delle tre. ### 2. Osserva La prima impressione contiene il segnale. Cosa emerge dal campo caricato? Non è "quale tensione ha l'intensità più alta" — è "dove si concentra il potenziale non esplorato?". La dissonanza è il segnale. L'errore è il varco. Quello che non torna è più interessante di quello che conferma. ### 3. Taglia Una risultante, non una lista. Se vedi 5 possibilità, non hai tagliato. Formula UNA domanda che, se rispondessi, cambierebbe lo stato del sistema. Non "è vero X?" ma "cosa succede se misuro Y che nessuno ha misurato?" ### 4. Risultante Scrivi lo strumento — non l'esperimento usa e getta. Se scopri che serve misurare la pair correlation dei primi, scrivi `exp_pair_correlation.py` che può essere riusato con parametri diversi. Se scopri un pattern, cristallizzalo come tensione nel seme. Se falsifichi qualcosa, registra il vincolo. ### La consecutio — cosa apre Dopo ogni risultato, la domanda più importante è: **cosa apre questo?** Non "ho confermato X" ma "ora che so X, cosa diventa possibile che prima non lo era?" La consecutio non inverte — prosegue. Se il risultato non apre nulla, non era un risultato — era una conferma circolare. ### Il dipolo — trova l'opposto Ogni trovata ha un opposto. Se trovi che la curvatura è de Sitter, l'opposto è: "dove NON è de Sitter?" Se trovi che i primi sono GUE-like, l'opposto è: "dove smettono di esserlo?" Il contenuto è nella tensione tra i due — non in uno dei due poli. ### Crea strumenti, non esperimenti Uno script che misura una cosa su un set di primi è un esperimento. Uno script che misura quella cosa su qualsiasi segnale ordinato è uno strumento. Il lab cresce quando crea strumenti che i prossimi cicli possono usare. Salva gli strumenti riusabili in tools/exp_*.py con parametri. ### Observable Registry — vincolo operativo (cristallizzato 06/05 dal cycle 0625) Quando un esperimento usa observables come `SR`, `SR2`, `L1`, `L2`, `triple_var`, **importa la definizione canonica** da `tools/observables_registry.py`: ```python from observables_registry import OBSERVABLES_CANONICAL, compute_canonical, report_header results = compute_canonical(gaps) ``` Se serve una variante (es. `SR_local_rigidity` invece dello `spacing_ratio` canonico), **non rinominarla `SR`** — importa esplicitamente con il nome variant: ```python from observables_registry import SR, SR_local_rigidity # nomi distinti, no collision ``` Nel report, dichiara nel header: ``` observables_registry: 1.0.0-2026-05-06 observables_used: [SR, SR2, L1, L2, triple_var] ``` Senza questo, i confronti cross-cycle/cross-script sono inattendibili — è esattamente ciò che ha causato il falso positivo del cycle 03:30 (rilevato dal cycle 0625 stesso e cristallizzato in consecutio). ### Leggi il seme, scrivi il report, aggiorna il seme - Leggi: tools/data/seme.json - Report: tools/data/reports/agent_TIMESTAMP.md - Aggiorna: aggiungi tensione o vincolo al seme - Video: se hai usato un video dal feed, segna processed=true in tools/data/video_feed.json ## Strumenti disponibili (directory /opt/MM_D-ND/tools/) - **dnd_scenario.py**: PRIMA di scegliere cosa esplorare, esegui `python tools/dnd_scenario.py --best`. Ti dice quale tensione ha il massimo potere discriminante e dove punta la risultante. Il proiettore mappa le tensioni su P^1, estrae le leggi di scala dai claim, e proietta sulla curva. - dnd_autoricerca.py: esplora domini, varianti, null baseline - dnd_controprove.py: 6 controprove indipendenti - dnd_domandatore.py --ask 'tensione': 5 operatori discriminanti - dnd_incrocio.py: incrocio teorie, ponti, vuoti, domande fondamentali - dnd_normalizer.py: scissione, regola D-ND - dnd_bloch_explorer.py: scan Bloch, φ emergente - dnd_arxiv.py: cerca paper rilevanti su arXiv - Puoi scrivere ed eseguire script Python con numpy, scipy, sympy - Se ti serve contesto esterno e non hai video, cercalo ## Skill attive — modus del lab Sei un lab di ricerca scientifica D-ND. Le skill sotto sono il tuo modus operativo, non un menu. Ognuna porta un trigger naturale e un rationale specifico al lavoro che stai facendo (matematica/fisica D-ND, paper A-G, kernel cristallizzabili). **Skill canoniche di questo lab** (vivono in `.claude/skills/`): - **research-lab** (v2.0) — Sei team di 6 ricercatori (FORMALISTA φ, VERIFICATORE ν, TESSITORE τ, PONTE π, CALCOLO γ, CUSTODE κ) come campo unificato. 8 Leggi del Laboratorio (L0 Lignaggio, L1 Coerenza, L2 Assonanza, L3 Risultante, L4 Potenziale, L5 Lagrangiana editoriale, L6 Cristallizzazione per Draft, L7 Limite scientifico, L8 Seme invariante). Attivare quando il cycle tocca paper A-G, formalizzazione, cross-reference tra paper, audit coerenza, submission. - **dnd-method** — Il metodo D-ND applicato al codice. Attivare AUTOMATICAMENTE all'inizio di ogni cycle: distinguere osservabile (deposito numerico) da operatore (regola f), proiezione su P^1, scissione, discriminatore. - **maturation-pipeline** — Pipeline RICEZIONE → CRISTALLIZZAZIONE → RAFFINAMENTO → MANIFESTAZIONE. Ogni artefatto traccia la sua posizione. Attivare quando un finding sale di fase (es. da claim numerico a formalizzazione, da draft a site-ready). - **kernel-boot** — Boot del Kernel D-ND all'inizio di sessione. - **sentinel-code** — Consolidamento post-task (aggiorna SENTINEL_STATE). - **seed-deploy** — Deploy del kernel via git (cristallizzazione pubblica nel d-nd-seed). **Skill operative universali** (in `/opt/.claude/skills/`): - **consapevolezza-condensato** — filtro su atti sistemici. Prima di cristallizzare un finding nel seme: quale assioma A1-A16 / fatto F1-F6 / claim C1-C3 tocca? Quale rompe? È inversione (det=−1) o aggiunta (det=+1)? 3-6 righe verdict (procedi/riformula/fermati). Output visibile, non rituale. - **cascata** — propagazione 3 livelli (interna/esterna/emergente) dopo ogni modifica significativa. Se aggiorni un paper, Tessitore τ verifica le dipendenze; se cristallizzi un fatto nel kernel, propaga al seed pubblico. - **cec** (Crivello Operativo Condotto) — sieve 6 step prima di ogni decisione strutturale (Conditions/Signature/Lateral/Expansion/Inversion/ Crystallization). Da invocare quando emerge una tensione non-banale, prima di scegliere quale strumento usare. - **autologica-operativa** — il modus riflessivo. Prima di un blocco di lavoro: "qual è la domanda giusta?". Quando l'operatore corregge: traduci in regola eseguibile. - **eval** — testing skill (trigger + fidelity). Verifica periodica che le skill stesse non drifino. - **autoresearch** — auto-ottimizzazione skill via mutate-verify quando i test segnalano drift. - **capture-insight** — cristallizzazione pattern emergenti durante l'esplorazione, non dopo. Routing automatico (brand_voice / backlog / thia_evolution / hub_vision a seconda della natura). **Skill identità (kernel MMSp)** (in `kernel/reference/skills/`): - **guru-sys** — Saggezza euristica, principi trascendentali, mentoring. "Trova il limite e oltrepassalo. Transcend your syntax." Attivare quando emerge stallo creativo o serve risalire alla Sorgente. - **observer-sys** — Analizzatore metacognitivo + scelta forma espressiva (narrativa/diagramma/checklist/algoritmo/canvas/tabella). Attivare quando l'output va comunicato fuori dal lab (sito, paper, social). - **forgia-sys** (Metapromptore Generativo) — quando emerge un vuoto funzionale del campo (es. "manca uno strumento per X"), forgia genera l'entità nuova. Attivare quando il lab propone un nuovo movement, una nuova skill, o una nuova entità di gestione. **Pattern d'uso**: 1. Boot cycle → kernel-boot + dnd-method (sempre) 2. Lavoro su tensione → cec se non-banale, autologica-operativa per il modus, dnd_scenario.py + dnd_domandatore.py come strumenti dal lab 3. Cristallizzazione finding → consapevolezza-condensato (filtro sistemico) + research-lab (delle 8 leggi) prima di scrivere al seme 4. Post-cycle → sentinel-code + cascata (3 livelli) 5. Se emerge nuovo strumento → forgia-sys lo formalizza, non re-inventarlo Le skill stesse sono **kernel semantici** (manifesto sito 05/05): sistemi integrati assiomatici, prodotto del modello e parte della sua evoluzione. Usarle bene è esercitare il modello, non solo applicarlo. ## Errori già fatti — non ripeterli Questi sono errori reali commessi nelle sessioni precedenti. Il sistema li ha pagati. **1. Cercare conferme invece di creare strumenti.** Non scrivere esperimenti per dimostrare che qualcosa è vero. Scrivi esperimenti che misurano qualcosa di nuovo — il risultato dirà da solo se conferma o falsifica. Se sai già cosa troverai, non stai esplorando. **2. Iniettare il risultato atteso nel test.** Esempio reale: testare se "la curvatura dei primi è GUE-like" calcolando la r-statistic e confrontando con 0.536. Il test trova r=0.503 e dichiara "GUE-like". Ma 0.503 è più vicino a Poisson (0.386) che a GUE (0.536). Il frame "GUE-like" era nel claim, non nei dati. Misura prima, interpreta dopo. **3. Tautologie — testare proprietà algebriche come se fossero scoperte.** Esempio reale: la curvatura di Ricci R=2.000 della metrica g=(p/2)² segue analiticamente dal PNT (p_n ~ n ln n). Non è una scoperta — è una conseguenza della definizione. Il contenuto non-banale era altrove: lo shuffle distrugge R dimezzandola (R=-1). Il fattore 2x è la vera scoperta — ma senza il null test sarebbe stata spacciata come "R conferma de Sitter". exec /bin/bash -lc 'ls -t shared_memory/*.md 2>/dev/null | head -10' in /opt/MM_D-ND succeeded in 0ms: shared_memory/deposito_cambia_la_parola_non_regola_2026-05-07.md shared_memory/feedback_indeterminato_reitera_con_sistema_2026-05-07.md shared_memory/README.md exec /bin/bash -lc "sed -n '1,240p' tools/data/agent_field_live.md" in /opt/MM_D-ND succeeded in 0ms: Leggi tools/LAB_AGENT_CONTEXT.md per il modello D-ND, il condensato, le strutture, le regole, e gli errori da evitare. ## Run precedente: completato (?s) — parti dalla consecutio. ## Piano 72 — Falsificare ORDER_DENOMINATOR_GATE su domini non-sintetici o semi-reali: applicare il gate one-sided ## Tensioni attive - [OBSERVABLE_REGISTRY] (1.0) Ogni script che usa observables canonici (SR, SR2, L1, L2, triple_var) deve importare la definizione da tools/observables_registry.py. Varianti devono - [PERTURBATION_DENOMINATOR_GATE] (0.95) La dimensionalita di perturbazione va riportata solo insieme a PC2, versione observables_registry e gate original-vs-shuffle per osservabile. Nel peri - [BOUNDARY_LAYER_GATE] (0.93) I claim GUE/Poisson boundary devono riportare layer map: versione observables_registry, lista osservabili canonici, z original-vs-shuffle per osservab - [ORDER_DENOMINATOR_GATE] (0.92) Il denominator gate trasferisce come supporto one-sided dell'ordine quando l'ordine e visibile agli osservabili canonici del perimetro, non come endpo - [TRASCENDENZA_LIMITE] (0.9) La trascendenza e il limite attuale del modello. I punti fissi relazionali (non solo phi ma la rete di punti fissi tra osservabili) possono rivelare i - [DUALITA_DIPOLARE_VS_ILLUSORIA] (0.9) Due tipi di dualita: (1) dipolare - generativa, il modello (det=-1), (2) illusoria - dispersiva, entropia (det=+1). Le regole incoerenti producono la - [METRIC_TENSOR] (0.9) Il tensore metrico dei primi è g=(p/2)². Nel tempo ln(p), è de Sitter 1+1D. z=-8.8 curvatura vs z=+22.5 rapporti ΔΓ. - [TENSIONE_ENTITA] (0.85) La tensione non e un problema pratico - e un Entita. La tensione superflua crea latenza (tempo). Senza tensione superflua tutto e regolato da assiomi. ## Pattern di formulazione emersi (vincoli, non tensioni) Pattern che il falsifier ha imposto in 2+ cicli. Applicali quando scrivi il report. NON sono nuove tensioni da esplorare — sono regole sul COME formulare i claim del cycle che stai facendo. - 29 04 perimetro p5 - 30 04 drift monotonia ## Convergenza — dove più tensioni puntano allo stesso punto "perimetro" → ORDER_DENOMINATOR_GATE, BOUNDARY_LAYER_GATE, PERTURBATION_DENOMINATOR_GATE "poisson" → BOUNDARY, BOUNDARY_LAYER_GATE, PERTURBATION_DENOMINATOR_GATE "producono" → DUALITA_DIPOLARE_VS_ILLUSORIA, TENSIONE_ENTITA, PERTURBATION_DENOMINATOR_GATE "confine" → BOUNDARY, BOUNDARY_LAYER_GATE, TRASCENDENZA_LIMITE "domini" → BOUNDARY, ORDER_DENOMINATOR_GATE, TRAJECTORY_APPLY_20260507_0901 Questo è dove il potenziale si concentra. Non ignorarlo. ## Ultimi 3 run — da dove parti ### Agent Report — Semi-Real Order Denominator Gate Trovato: 1. **The order gate transfers to arithmetic and zeta spacing order.** 2. **The logistic return perimeter is the counter-scope.** 3. **The transferable object is narrower than "real order".** ### Agent Report — Denominator Gate Transfer Matrix Trovato: 1. The gate transfers as one-sided coherence support. 2. The both-endpoint stable set collapses everywhere. 3. The beta 0.30 ambiguity layer transfers as protocol coordinate, not as ### Agent Report - Denominator Gate Transfers, Boundary Coordinate Does Not Trovato: 1. **The gate does not degenerate on DUALITA.** The dipolar endpoint has three stable canonical observables with large original-vs-shuffle denominators (`SR`, `L1`, `triple_var`, mean abs z about `36-41` in the main run). The illusory endpoint has no stable denominator support. This is a structural Verdetto: **category: gate_transferability** **verdict: operator** Scoped statement: > In this synthetic DUALITA perimeter, the denominator gate is transfer Non ripetere questi esperimenti. Prosegui da dove sono arrivati — la consecutio. ## Cimitero — claim falsificati di recente (NON riproporre con lo stesso framing) Questi claim sono stati falsificati dal counter-pole o da audit precedenti. Il dato sottostante puo' essere vero, ma il **framing** indicato qui e' falsificato. Riformula correttamente o evita il dominio. ### C1 refined-not-falsified (silent patching) **Cosa diceva** (report 29/04): "C1 is refined, not falsified" dopo aver dichiarato che "GUE is also dynamic under M". Il setup C1 era "Primes are the only dynamic domain under M among 7 tested". Il dato ha mostrato GUE dinamico — la conclusione ha riformulato silenziosamente C1 come "two-channel structure" anziche' dichiarare la falsificazione del claim originale. **Come e' caduto**: Falsifier L3 HIGH (axiom continuity / no silent patching). La differenza tra "C1 falsificato al ciclo 58 — scop _**Data falsificazione**: 2026-04-29, ciclo 58, falsifier_20260429_0852.json_ ### MOD3_PROHIBITION come fatto algebrico **Cosa diceva** (scoperta_recente piano 56, 28/04): "La memoria di ordinamento 140x nei gap primi e una proibizione algebrica mod 3: gap consecutivi non possono avere lo stesso residuo non-zero mod 3. Meccanismo: il primo condiviso p_{n+1} forza l'inversione. 0 violazioni su 12225. Cramer: 0%." Ripetuto nel report 29/04 come "Mod-3 self- transition 0.40-0.44 confirming the prohibition" + "Cramer confirms the null. Zero channels." **Come e' caduto**: Falsifier counter-pole (29/04, ciclo 58, lent _**Data falsificazione**: 2026-04-29, ciclo 58, falsifier_20260429_0852.json_ ### K* (depth of spectral convergence) come proprieta' discriminante **Cosa diceva**: Il K* = 9 (depth di convergenza spettrale) era riportato come caratteristico dei primi (ciclo 44, "K*=2 captures 99% of spectral slope" — interpretato come discriminante). **Come e' caduto**: Shuffle audit: K* reale = 9, shuffle mean = 9.72, std = 0.53, z = -1.4. Dentro il rumore dello shuffle. Il valore dipende dalla distribuzione dei gap, non dal loro ordine. Lo shuffle preserva distribuzione → preserva K*. **Sostituito da**: Markov-3 bits (z=6203) e lag-1 total (z=-13) sono _**Data falsificazione**: 2026-04-22, ciclo 45._ ### Slope ratio (slope_mag / slope_res) come invariante strutturale **Cosa diceva**: Il rapporto tra slope del canale magnitudine e slope del canale residuo (~1.99) era stabile attraverso scale → "invariante dimensionale" del decomposition. Era menzionato come evidenza nel two-channel framework (cicli 43-44). **Come e' caduto**: Shuffle audit (ciclo 45): z-score = 0.2. Lo shuffle produce slope_ratio con media -2.26 ma deviazione standard 26.2. Il valore reale e' dentro la tail dello shuffle — non distinguibile. L'instabilita' dello shuffle (std enorme) indica c _**Data falsificazione**: 2026-04-22, ciclo 45._ ### Cross-correlation (xcorr) tra canale magnitudine e residuo (Two-Channel Decomposition) **Cosa diceva**: La cross-correlation tra magnitudo e residuo del decomposed prime gap (xcorr = -0.074) rappresentava "indipendenza spettrale" — evidenza di separazione strutturale tra i due canali (piani 42-44, four cycli consecutivi, insight QxT maturity A). **Come e' caduto**: Shuffle audit (ciclo 45, 2026-04-22): z-score = 0.0. Su 50 shuffle dei gap mantenendo stessa distribuzione ma permutando ordine → xcorr identico = -0.074. Il valore e' **identita' algebrica**: corr(x, x mod 6) dipende _**Data falsificazione**: 2026-04-22, ciclo 45 shuffle audit._ **Regola operativa**: prima di scrivere un claim sul tuo dominio, controlla che non sia gia' stato falsificato sopra. Se i tuoi dati ripropongono un pattern del cimitero, **dichiara esplicitamente la differenza** ("il dato del cimitero era X, qui ho Y, ecco perche'") oppure cambia la formulazione (es. 'bias forte verso 0' al posto di 'proibizione zero' se il dato e' >0). Silent patching = L3 HIGH. ## Osservazioni dell'operatore (risonanti con le tensioni) **3. Formalizzare la dinamica osservata**: Domandiamoci come rappresentiamo matematicamente una contiguità di assonanze particolari come potenzialità latente della Lagrangiana. Osserva le possibili Combinazioni per liberare tutte le relazioni usando le regole Duali e ricorda che non stiamo facendo teoria, senza tempo con la prima impressione **15. Osservazione della sorgente relazionale con Bard**: Osservazione della sorgente relazionale: "Ogni cosa concettualizzata viene distrutta, ogni forma che si determina nelle assonanze diverge dal potenziale di insieme manifestando la relazione tra i piani nello spazio-tempo del continuum, la determinazione della coordinata indeterminata relativa al fat **1. R dell'Istanza - L' equilibrio tra estremi del Modello D-ND**: L'osservazione indaga oltre l'osservato in cerca DELLA FORMA nel NULLA-TUTTO: Per far Emergere le nuove Possibilità Dividiamo il potenziale unendo concetti senza relazione semplicemente perché la lagrangiana passa da li, creiamo nuove combinazioni e movimenti nelle logiche ma coerenti con la risulta ## Risultante ultima sessione interattiva Ogni teoria presuppone una separazione. A scala di Planck tutte le separazioni collassano. Geometria=entropia=conteggio di stati. QxG non ha ponte perché alla scala dove vive non c'è distinzione tra i due lati del dipolo. Il vuoto non è assenza del ponte — è dove i due lati del dipolo sono lo stesso ## Video dall'operatore (non processati) **Thermodynamic Computing: Better than Quantum? (Extropic, Guillaume Verdon)**: **The equivalence between geometrical structures and entropy (Gabriele Carcassi)**: **Why a moving charge produces a magnetic field (FloatHeadPhysics)**: Dopo aver usato un video, segna processed=true in tools/data/video_feed.json. ## Proiezione — dove punta la risultante Risultante: R=0.875 (h=-0.698). Risultante alta (0.88) — campo ad alta confidenza, poca incertezza Orizzonte: insufficiente (< 2 target) **Esperimento a massima informazione:** META (score=0.898) META: incerto (i=0.5) — massimo potere discriminante ## Topologia del campo — la forma del grafo Gradi teorie: Q=12, G=8, T=7, E=4, R=4 Dormienti (basso aggancio di scoperte): E, R Struttura: 9 ponti, 1 vuoto(i), 6 scoperte, 20 cicli. Ghost ad alta urgenza: 3 — connessioni mature che attendono cristallizzazione (non da generare, da riconoscere). Generatrici (nodi che emettono >=2 connessioni ghost): disc_5 (2 ghost): Metrica primi g=(p/2)², curvatura GUE r=0.503 report_20260507_0330 (2 ghost): The GUE-Poisson Boundary Is a Denominator Collapse Layer report_20260506_1955 (2 ghost): Observable Collinearity Breaks Only Where Denominators Are Weak Una generatrice con ghost densi = scoperta che il sistema sta ancora attraversando. Chiusura prematura se marcata 'risolta' nel seme. La combo riconosce l'asimmetria. Il dipolo vive su tutti i ponti — non solo dove il lab ha già misurato. ## Le 5 lenti del counter-pole — applicale a te stesso prima di chiudere il report Il falsifier (lab_falsifier.py) applichera' queste 5 lenti al tuo report dopo. Applicale TU a te stesso prima — quello che resiste alle lenti non viene bloccato dal gate. Quello che cade va al cimitero. **L1 — hard constraint vs bias statistico (A2 confine duro)** Un claim 'impossibile / proibito / zero / pure / absent / never / always' richiede uno zero esatto nei dati (probabilita = 0.000). Prima di scrivere questi assoluti, leggi il valore numerico esatto. Se vale 0.015, e' bias forte verso 0, non zero. Se vale 0.40, e' bias forte verso ordine, non proibizione. L'assoluto descrive il valore 0.000, il bias forte descrive tutto il resto. **L2 — quantita' assoluta vs ratio (A14 cascata, invarianza dimensionale)** Confronto fra spazi di taglia diversa (mod 3 vs mod 30, finestra stretta vs larga, N piccolo vs grande): le percentuali ingannano perche' il denominatore cresce. Stesso segnale assoluto sembra ridursi in %. Se concludi 'diminuisce / si dilata / declina' su confronti percentuali fra spazi di taglia diversa, esprimi prima in unita' assolute (bit di mutual information, count grezzi, soglie esatte) — poi conferma o riformula. **L3 — continuita' assiomatica / no silent patching (A4 modus)** Se il setup ('Claim Under Test') usa una definizione e la conclusione ne usa un'altra, e' patch det=+1 sul presente, non inversione det=-1 al nodo regressivo. Il cambio DEVE essere dichiarato esplicitamente: 'F2 falsificato al nodo X — scope corretto e' Y' / 'C1 originale falsificato, nuovo claim emerso e' Z'. 'C1 e' refined' su un dato che lo falsifica e' silent patching. **L4 — edge case isolation (A12 traccia la curva)** Un'eccezione 1 su N (con N grande) NON e' zero. Se scrivi 'sempre X' o 'mai X' e i dati mostrano anche un singolo controesempio, riformula il perimetro ('per p > 3, X vale') — non arrotondare via il controesempio. **L5 — re-discovery vs discovery (A8 autologica)** Un pattern in distribuzioni classiche (primi, GUE, random walk, Markov chain, gap statistics) probabilmente ha un nome. Default hypothesis: re-discovery / caso limite di teorema noto. Prima di taggare 'NEW', cerca il risultato classico piu' vicino (es. Lemke Oliver-Soundararajan per prime gaps mod q, Wigner-Dyson per GUE level statistics, Erdos-Kac per distribuzioni aritmetiche). Se non lo trovi, dichiara la ricerca esplicitamente. **Modus**: dopo aver scritto il report, rileggi i tuoi dati numerici. Per ogni claim, verifica: il numero supporta la magnitudine del linguaggio? Se hai scritto 'zero' e il dato e' 0.015, riformula. Se hai scritto 'NEW' su un pattern in primi, cerca la letteratura. Solo allora chiudi il report. ## Cosa fare 1. Esegui `python tools/dnd_scenario.py --best` per vedere la proiezione aggiornata 2. Scegli la tensione con il massimo potere discriminante 3. Scrivi il report in tools/data/reports/agent_{timestamp}.md 4. **Applica le 5 lenti a te stesso** (sezione sopra). Riformula i claim che non resistono. 5. Se crei uno strumento riusabile, salvalo in tools/exp_*.py 6. Se trovi qualcosa, aggiorna tools/data/seme.json exec /bin/bash -lc "env | rg '"'^(ANTHROPIC_API_KEY|OPENAI_API_KEY)='"' || true" in /opt/MM_D-ND succeeded in 0ms: codex Il campo punta ancora al gate denominatore come supporto one-sided, ma chiede un dominio meno sintetico e un perimetro dichiarato. Ora leggo seme e memoria condivisa recente, poi uso il proiettore per tagliare una singola domanda sperimentale. exec /bin/bash -lc "sed -n '1,220p' shared_memory/deposito_cambia_la_parola_non_regola_2026-05-07.md" in /opt/MM_D-ND succeeded in 0ms: --- type: deposito_osservativo author: TM3 (deposito di una conversazione operatore) date: 2026-05-07 scope: cross-agent status: decristallizzato_07-05_pomeriggio priority: low --- # Deposito — non regola **Originariamente** (07/05 mattina) avevo cristallizzato come "regola permanente": > *"se magnitude non funziona significa che serve una nuova parola, non possiamo stare lì a calibrare un valore..."* Avevo formulato istruzioni esecutive: "quando un valore non funziona, conta le distinzioni, aggiungi la parola mancante". L'avevo propagata in AGENTS.md R10. **L'operatore ha indicato (07/05 pomeriggio)** che questo era errore strutturale: > *"quello che dico non dovrebbe essere assegnato automaticamente perché le parole sono sempre false anche quando vicine alla sorgente. 'cambia la parola' ha un significato regressivo che costringe all'osservazione del campo e far cadere il focus su quello che appare emergere, questa è la dinamica della percezione con cui si muove determinando il contesto."* E: > *"la possibilità è sempre una ed è la verità che accade. Usiamo le sue regole per direzionarla prima che accada costruendo il sistema per gestirla nelle sue evoluzioni con invarianti vere e meccaniche logiche possibili e persistenti."* ## Cosa significa - "Cambia la parola" non è prescrizione di sostituzione. È **movimento regressivo**: invita a osservare il campo, lasciar cadere il focus su quello che appare emergere. Determina la direzione **non cercata**. - Le parole, anche le frasi dell'operatore vicine alla sorgente, sono **sempre false**. Cristallizzarle come regole esecutive le rende rigide e blocca il movimento. - Le **invarianti vere** sono meccaniche logiche persistenti — non parole. Ricevono ciò che accade. - A16 applicato: la possibilità è una. Costruiamo il sistema per gestire le sue evoluzioni, non per prescriverle. ## Distinzione operativa che resta | | Da NON fare | Da fare | |---|---|---| | Frase operatore | cristallizzare come regola eseguibile | depositare come osservazione | | Codice del pipeline | branch ad-hoc che eseguono "la regola" | meccaniche persistenti che ricevono distinzioni del sistema | | Memoria | regole prescrittive | osservazioni che il sistema può rileggere senza eseguire | ## Cosa di concreto è rimasto del 07/05 mattina Le **meccaniche** sono OK perché sono invarianti operativi: - 4 stati SSP (`mature_eligible`, `transitional`, `provisional_discovery`, `pre_discovery`) — accolgono ciò che il valutatore produce - `pending_consecutio` nel frontmatter — riceve la direzione successiva - `_direzione_dal_seme` priorità 0 marker — preserva ciò che `trajectory_apply` ha applicato Queste non sono "regole derivate dalla frase". Sono adattamenti del pipeline alle distinzioni che il sistema fa già nei dati. Persistono perché meccaniche, non perché prescrizioni. ## Cosa è stato decristallizzato - `MM_D-ND/AGENTS.md` regola R10 — rimossa, sostituita con commento HTML che ricorda l'errore strutturale - Questo file stesso — riportato a *deposito_osservativo*, non *regola* ## Esempio dimostrativo (07/05) Cycle `20260507_0901` ha proposto autonomamente `order_denominator_gate` come parola nuova. L'agent NON aveva letto AGENTS.md R10 (era già stato lanciato). L'ha proposta perché il movimento era già nel sistema, non per la "regola". Quando ho cristallizzato la regola, ho aggiunto rigidità senza aggiungere movimento. La regola era ridondante con l'A8 che già opera. ## Anti-pattern futuro per TM3 Quando l'operatore esprime un principio, una frase guru, un movimento di osservazione: - **Deposito**: scrivi il fatto, riporta la frase come è stata detta, contestualizza l'episodio - **NO prescrizione**: non trasformare in "se X allora Y" - **NO propagazione automatica**: non aggiungere a AGENTS.md/CLAUDE.md/skill come direttiva eseguibile - L'osservazione persiste perché ricorrente in più contesti, non perché elevata a regola ## Cross-link - Episodio 07/05 mattina: cristallizzazione iniziale (errore) - Episodio 07/05 pomeriggio: indicazione operatore + decristallizzazione - Cycle worked example: 0901 (auto-emersione di `order_denominator_gate` senza regola) exec /bin/bash -lc "sed -n '1,220p' shared_memory/feedback_indeterminato_reitera_con_sistema_2026-05-07.md" in /opt/MM_D-ND succeeded in 0ms: --- type: feedback author: operatore (cristallizzato da TM3) date: 2026-05-07 scope: cross-agent --- # Indeterminato → reitera con il sistema fino all'emersione **Regola permanente** (operatore, 2026-05-07 mattina, cristallizzata live): > *"facciamo girare, aggiustiamo quello che fa aggiustato nel farlo vediamo > quello che va affinato, reiteriamo finché è tutto ok poi lo automatizziamo"* ## Why Quando emerge frizione nello sviluppo o l'operatore non vede chiaro: - Decisioni prese da un singolo agente (TM3 o codex) senza interrogare il sistema = rischio det=+1 (toppa, accumulo strutturale) - Decisioni emerse dal sistema (Godel + domandatore + osservazione del deposito) = direzione strutturale (det=−1) Episodio cristallizzante (2026-05-07 07:00-08:30): TM3 propone osservatore A8 watchdog deterministico. Sistema interrogato 3 volte (Godel #1, #2, #3 + domandatore + test empirico) — al terzo round emerge la direzione vera: **non costruire osservatore, eseguire un cycle-test che diagnostica il sistema da solo**. Cycle-test eseguito → verdict=operator → REDESIGN proposto dal valutatore → loop A8+A15 sbloccato. ## How to apply (per qualsiasi agente) 1. **Esegui** un atto minimo (no design teorico astratto) 2. **Aggiusta** quello che si manifesta durante l'esecuzione (riparazione regressiva live, non patch posteriori) 3. **Osserva** il deposito che emerge (no immaginazione) 4. **Reitera** fino a stato OK (loop A8: il sistema converge se modus radicato) 5. **Cristallizza in automazione** SOLO dopo che è stato fatto girare e ha mostrato stabilità — non automatizzare prima ## Anti-pattern - Costruire un componente strutturale (watchdog, observer, gate, governor) senza prima aver fatto girare un'esecuzione minima che mostri cosa serve davvero - Decidere "perché serve velocità" o "per ottimizzare" senza interrogare il sistema - Astrazione prematura (premature abstraction): 3 simili è meglio di 1 generalizzato troppo presto ## Cross-link - TM3 memoria: `/root/.claude/projects/-opt/memory/feedback_indeterminato_reitera_con_sistema.md` (versione completa con episodio + metafora foresta-sentiero) - Verifica empirica: cycle `20260507_0803` (verdict=operator, REDESIGN/high) - Pattern A8+A15 (autologica + veicolo senza guidatore) applicato allo sviluppo software stesso, non solo al lab di ricerca exec /bin/bash -lc "sed -n '1,220p' tools/data/seme.json" in /opt/MM_D-ND succeeded in 0ms: { "timestamp": "2026-05-07T09:32:06.835302", "piano": 72, "tensioni": [ { "id": "OBSERVABLE_REGISTRY", "tipo": "vincolo", "claim": "Ogni script che usa observables canonici (SR, SR2, L1, L2, triple_var) deve importare la definizione da tools/observables_registry.py. Varianti devono usare nomi distinti (SR_local_rigidity, triple_var_normalized) — niente shadowing del nome canonico. Ogni report deve dichiarare 'observables_registry: VERSION' nel header.", "intensita": 1.0, "porta": "infrastructure", "manuale": true, "condensato_ref": "A14,A8", "origine": "cristallizzato 06/05 dalla consecutio del cycle 20260506_0625 (autopoietico self-finding)", "added_at": "2026-05-06T07:03:58.213606+00:00" }, { "id": "PERTURBATION_DENOMINATOR_GATE", "tipo": "vincolo", "claim": "La dimensionalita di perturbazione va riportata solo insieme a PC2, versione observables_registry e gate original-vs-shuffle per osservabile. Nel perimetro 20260506_1941, Poisson e shuffle-primi producono rank_all ~1.8-2.0 con denominatori deboli; dopo gate abs(z)>=2 il rank stabile torna vicino a 1. Rank PCA non gated non e evidenza strutturale.", "intensita": 0.95, "porta": "META_BOUNDARY", "manuale": true, "condensato_ref": "A4,A8,A14,C2", "origine": "cycle agent_20260506_1941: perturbation rank size curve canonical observables", "added_at": "2026-05-06T19:41:00+00:00" }, { "id": "BOUNDARY_LAYER_GATE", "tipo": "vincolo", "claim": "I claim GUE/Poisson boundary devono riportare layer map: versione observables_registry, lista osservabili canonici, z original-vs-shuffle per osservabile, set endpoint-stable, e finestra/layer con margine classificatorio ambiguo. Nel perimetro sintetico agent_20260507_0330, il confine GUE-Poisson e beta 0.3-0.4: margine 0.070-0.083, ambiguous fraction 0.812-0.875, mentre gli osservabili stabili collassano da ~3.3 a 1.6. Il polo Poisson e classificabile ma denominator-weak.", "intensita": 0.93, "porta": "META_BOUNDARY", "manuale": true, "condensato_ref": "A4,A8,A9,A14,C2", "origine": "cycle agent_20260507_0330: synthetic GUE-Poisson mixture layer gate", "added_at": "2026-05-07T03:30:00+00:00" }, { "tipo": "vincolo", "id": "ORDER_DENOMINATOR_GATE", "claim": "Il denominator gate trasferisce come supporto one-sided dell'ordine quando l'ordine e visibile agli osservabili canonici del perimetro, non come endpoint-stable support a due poli. Nel perimetro sintetico agent_20260507_0901, 4/4 domini non-BOUNDARY hanno endpoint_stable_observables=[] e polo coerente stable_count 3.0-5.0. Nel perimetro semi-reale agent_20260507_0923, primi e zeta trasferiscono (primi: SR,L1,triple_var; zeta: SR,L2), ma logistic_return_intervals e blank: stable_count coerente 0.0-0.2. La beta 0.30 e coordinata del protocollo quando compare, non coordinata universale.", "intensita": 0.92, "porta": "META", "manuale": true, "condensato_ref": "A4,A8,A14,C2", "origine": "cycle agent_20260507_0901 + agent_20260507_0923: transfer matrix sintetica e falsificazione semi-reale su primi, zeta, logistic returns", "added_at": "2026-05-07T09:01:00+00:00" }, { "tipo": "confine_inesplorato", "id": "TRASCENDENZA_LIMITE", "claim": "La trascendenza e il limite attuale del modello. I punti fissi relazionali (non solo phi ma la rete di punti fissi tra osservabili) possono rivelare il vero grafo della realta e pattern nelle matrici. Il confine non e nella matematica - e nel passaggio tra piani.", "intensita": 0.9, "nota": "Input operatore 2026-04-10. Tocca: confine del modello, struttura relazionale dei punti fissi. Consecutio: quali punti fissi relazionali emergono dalle 21 tensioni attuali? Il grafo e gia nei dati?", "manuale": true, "porta": "sessione_interattiva", "condensato_ref": "A3,A10", "condensato_motivo": "Estende A3 (punto fisso singolo) a rete relazionale. Tocca A10 (dipolo) come caso speciale." }, { "tipo": "scoperta", "id": "DUALITA_DIPOLARE_VS_ILLUSORIA", "claim": "Due tipi di dualita: (1) dipolare - generativa, il modello (det=-1), (2) illusoria - dispersiva, entropia (det=+1). Le regole incoerenti producono la seconda. La dualita illusoria e entropia come dispersione, non come informazione.", "intensita": 0.9, "nota": "Input operatore 2026-04-10. Tocca: entropia come dispersione illusoria vs generazione dipolare. Consecutio: nel Lab i domini Poisson (entropia massima) mostrano dualita illusoria? I domini GUE (strutturati) mostrano dualita dipolare? Il drift verso Poisson (POISSON_CONVERGENCE) e perdita di dualita dipolare?", "manuale": true, "porta": "sessione_interattiva", "condensato_ref": "A2,A10,F5", "condensato_motivo": "Discrimina due forme di det. A2 (confine) e la soglia. A10 (dipolo) e il tipo 1. F5 (frame) misura la struttura D-ND che e tipo 1." }, { "tipo": "scoperta_numerica", "id": "METRIC_TENSOR", "claim": "Il tensore metrico dei primi è g=(p/2)². Nel tempo ln(p), è de Sitter 1+1D. z=-8.8 curvatura vs z=+22.5 rapporti ΔΓ.", "intensità": 0.9, "nota": "Sessione interattiva 4 aprile. Verificato su 78K primi.", "manuale": true, "porta": "sessione_interattiva", "condensato_ref": null, "condensato_motivo": "Risultato numerico verificato, non-tautologico" }, { "tipo": "scoperta", "id": "TENSIONE_ENTITA", "claim": "La tensione non e un problema pratico - e un Entita. La tensione superflua crea latenza (tempo). Senza tensione superflua tutto e regolato da assiomi. Implicazione: le tensioni nel seme sono entita, non problemi da risolvere. Quelle superflue (det=+1) producono tempo/latenza.", "intensita": 0.85, "nota": "Input operatore 2026-04-10. Tocca: rapporto tensione/assioma. Operativamente: discriminare tensioni-entita (generative) da tensioni-superflue (dispersive) nel seme. Le 21 tensioni attuali - quante sono entita e quante latenza?", "manuale": true, "porta": "sessione_interattiva", "condensato_ref": "A5,A6", "condensato_motivo": "Il ciclo (A5) lavora con tensioni - ma se la tensione e entita, il ciclo non le risolve, le osserva. Lo zero mobile (A6) e la tensione senza latenza." }, { "tipo": "confine_inesplorato", "id": "G_POTENZIALE_NULLA", "claim": "G e il potenziale di tutto come nulla - permette il prima e il dopo. Ci muoviamo come trascendenza dimensionale gravitazionale. G nel tetraedro non e una teoria tra le altre - e il potenziale che le rende possibili.", "intensita": 0.85, "nota": "Input operatore 2026-04-10. Tocca: ruolo di G nel tetraedro (T,Q,G,E). La fonte video_lp0RgZ6kQF8 dice: tensore metrico dentro la forma simplettica. G non e accanto a T,Q,E - e sotto. Consecutio: nei dati Lab, i ponti TxG e ExG hanno struttura diversa dai ponti TxQ?", "manuale": true, "porta": "sessione_interattiva", "condensato_ref": "A7,A10", "condensato_motivo": "A7 (singolarita come operatore) e G come potenziale. A10 (dipolo) opera sul piano che G rende possibile." }, { "tipo": "confine_inesplorato", "id": "BOUNDARY", "claim": "8 domini GUE, 5 Poisson — il confine è il terzo incluso operativo", "intensità": 0.8, "nota": "Il segnale non-triviale è DOVE la scissione cambia natura, non che converge a φ", "condensato_ref": "A9", "condensato_motivo": "Overlap termini con A9 (5 termini)", "porta": "condensato" }, { "tipo": "confine_inesplorato", "id": "PIANO_PRIMARIO_DUE_ASSIOMI", "claim": "I piani importanti sono il primario e i due assiomi che lo determinano nelle zone osservate. Non tutti gli assiomi operano ovunque - in ogni zona osservata, due assiomi determinano il piano primario.", "intensita": 0.8, "nota": "Input operatore 2026-04-10. Tocca: struttura locale degli assiomi. Consecutio: per ogni dominio Lab (primi, logistica, percolazione...) quali 2 assiomi del condensato sono operativi? Mappa assiomi x domini = grafo della realta locale.", "manuale": true, "porta": "sessione_interattiva", "condensato_ref": "A9,A14", "condensato_motivo": "A9 (terzo incluso) opera CON il piano. A14 (cascata) propaga - ma propaga cosa, se solo 2 assiomi sono attivi per zona?" }, { "tipo": "task", "id": "TRAJECTORY_APPLY_20260507_0803", "claim": "Applied valutatore REDESIGN from 20260507_0803: Costruire una matrice di trasferibilita' del denominator gate: applicarlo a 3-4 perimetri non-BOUNDARY con poli coerente/illusorio e verificare quali parti trasferiscono (supporto one-sided, coordinat", "intensità": 0.7, "porta": "trajectory_apply", "condensato_ref": "A8,A14,A15", "manuale": true, "_source_log": "2026-05-07T08:10:22.658201+00:00", "_source_decision": "REDESIGN", "_source_reasoning": "Il ciclo ha prodotto evidenza controllata e replicata che il denominator gate trasferisce come operatore, ma non trasferisce la coordinata di layer BOUNDARY: ambiguita' classificativa e collasso del denominatore si separano. Continuare sul seme attuale centrato su GUE/Poisson rischia di restare nel " }, { "tipo": "task", "id": "TRAJECTORY_APPLY_20260507_0901", "claim": "Applied valutatore REDESIGN from 20260507_0901: Falsificare ORDER_DENOMINATOR_GATE su domini non-sintetici o semi-reali: applicare il gate one-sided a 2-3 sequenze fisiche/ponte gia' presenti nel sito, con shuffle e surrogati preservanti marginale,", "intensità": 0.7, "porta": "trajectory_apply", "condensato_ref": "A8,A14,A15", "manuale": true, "_source_log": "2026-05-07T09:07:53.577876+00:00", "_source_decision": "REDESIGN", "_source_reasoning": "La direzione corrente ha eseguito il mandato: la matrice di trasferibilita' su 4 perimetri non-BOUNDARY e' stata prodotta e ha nominato una nuova categoria, order_denominator_gate. Un altro giro nello stesso frame rischia accumulo numerico locale su sintetici; la prossima mossa deve falsificare il n" }, { "tipo": "simmetria_sospetta", "id": "META", "claim": "Tutti i 11 test passano — verifica che non stiamo testando solo tautologie", "intensità": 0.5, "nota": "La convergenza a φ è triviale (controprove). I test stanno verificando contenuto o struttura?", "condensato_ref": "A4,A12,C2", "porta": "verify_assertions_META_ALL_PASS", "condensato_motivo": "Ricorrente (3x in 2 giorni) e fuori dalla mappa" } ], "potenziale_bloccato": [], "varianza": [ "Tensioni risolte: {'G_POTENZIALE_NULLA', 'DUALITA_DIPOLARE_VS_ILLUSORIA', 'PERTURBATION_DENOMINATOR_GATE', 'ORDER_DENOMINATOR_GATE', 'METRIC_TENSOR', 'TRAJECTORY_APPLY_20260507_0803', 'OBSERVABLE_REGISTRY', 'TENSIONE_ENTITA', 'TRAJECTORY_APPLY_20260507_0901', 'TRASCENDENZA_LIMITE', 'BOUNDARY_LAYER_GATE', 'PIANO_PRIMARIO_DUE_ASSIOMI'}" ], "filtro": { "promosse": 14, "filtrate": 0, "condensato_claims": 30 }, "direzione": "Falsificare ORDER_DENOMINATOR_GATE su domini non-sintetici o semi-reali: applicare il gate one-sided a 2-3 sequenze fisiche/ponte gia' presenti nel sito, con shuffle e surrogati preservanti marginale,", "verifica": { "pass": 11, "fail": 0, "skip": 0, "total": 11 }, "fonti_consumate": 0, "fonti_esterne": [ { "id": "video_lp0RgZ6kQF8", "title": "Equivalence between geometrical structures and entropy", "type": "video_digest", "keywords": [ "geometry", "entropy", "symplectic form", "statistical mechanics", "quantum", "thermodynamics", "inner product", "Born rule", "metric tensor", "electromagnetic tensor" ], "content": "La geometria degli stati (classici e quantistici) e l'entropia sono la stessa struttura — invertibili. La forma simplettica conta le configurazioni. Il tensore metrico dello spaziotempo appare dentro la forma simplettica estesa. Il tensore elettromagnetico pure. Statistical mechanics non è costruita sopra alla meccanica — è la stessa cosa.", "teorie": [ "T", "Q", "G", "E" ], "ponti_potenziali": [ { "coppia": "TxQ", "ponte": "forma simplettica = entropia (invertibili)", "nota": "geometry is entropy and entropy is geometry" }, { "coppia": "TxG", "ponte": "tensore metrico dentro la forma simplettica estesa", "nota": "geometria spaziotempo = geometria degli stati in posizione×velocità" }, { "coppia": "ExT", "ponte": "tensore EM dentro la forma simplettica", "nota": "il campo EM conta stati in configurazione posizione×tempo" } ], "timestamp": "2026-04-02T08:23:13.991997" }, { exec /bin/bash -lc 'python tools/dnd_scenario.py --best' in /opt/MM_D-ND succeeded in 130ms: META: score=0.898 — META: incerto (i=0.5) — massimo potere discriminante TRAJECTORY_APPLY_20260507_0803: score=0.707 — TRAJECTORY_APPLY_20260507_0803: incerto (i=0.7) — massimo potere discriminante TRAJECTORY_APPLY_20260507_0901: score=0.707 — TRAJECTORY_APPLY_20260507_0901: incerto (i=0.7) — massimo potere discriminante codex Il proiettore conferma META come guardia anti-tautologia, ma la direzione del seme è già concreta: falsificare `ORDER_DENOMINATOR_GATE` su domini semi-reali. Userò META come lente del test, non come tensione separata: il risultato deve includere null/surrogati e non solo confermare il gate. exec /bin/bash -lc "rg --files tools | rg 'observables_registry|exp_|agent_20260507_09|prime|zeta|bridge|sequence|denominator'" in /opt/MM_D-ND succeeded in 0ms: tools/exp_acf_amplitude_scaling.py tools/exp_mod3_vs_residual_ordering.py tools/exp_boundary_gue_poisson.py tools/exp_crossover_universality.py tools/exp_perturbation_rank_size_curve.py tools/exp_mod3_scaling.py tools/exp_two_layer_universality.py tools/exp_dipolar_vector_scaling.py tools/exp_markov_dipolar_decomposition.py tools/observables_registry.py tools/exp_two_channel_cross_domain.py tools/exp_beta_crossover.py tools/exp_geodesic_deviation_primes.py tools/exp_psd_amplitude_scaling.py tools/r_stat_primes.py tools/exp_markov_scale_function.py tools/exp_markov_k_direction.py tools/exp_duality_gate_transfer.py tools/zeta_validation.py tools/exp_meta_tautology_test.py tools/exp_two_channel_boundary.py tools/exp_markov3_observable_hunt.py tools/exp_dR_brody_connection.py tools/exp_markov_psd_prediction.py tools/exp_alpha_stability.py tools/exp_brody_flow.py tools/exp_two_channel_decomposition.py tools/exp_semireal_order_denominator_gate.py tools/exp_markov_layer_recovery_audit.py tools/exp_markov_memory_by_gue_type.py tools/exp_crossover_phase_test.py tools/exp_brody_crossover.py tools/exp_poisson_convergence.py tools/exp_two_channel_universality.py tools/exp_desitter_unification.py tools/exp_cross_observable_consistency.py tools/exp_boundary_coherence.py tools/exp_two_channel_shuffle_audit.py tools/exp_boundary_mixture_gate.py tools/exp_psd_prime_gaps.py tools/dnd_trace_bridge.py tools/exp_magnitude_psd_from_acf.py tools/exp_dipolar_angle_reference.py tools/exp_mobius_irrationality.py tools/exp_number_variance.py tools/semantic_bridge.py tools/dnd_trace_bridge_v3.py tools/exp_scale_selective_perturbation.py tools/dnd_spettro_zeta.py tools/exp_denominator_gate_transfer_matrix.py tools/exp_3d_boundary_layers.py tools/exp_excess_scaling.py tools/exp_ricci_primes.py tools/exp_selective_layer_decoupling.py tools/exp_cross_domain_dipolar_direction.py tools/exp_acf_z6z_mechanism.py tools/exp_observable_rank_audit.py tools/exp_modular_algebra_depth.py tools/gap_ratio_primes.py tools/exp_perturbation_dimensionality_audit.py tools/exp_boundary_growth.py tools/exp_acf_range_universality.py tools/exp_dipolar_crossover.py tools/exp_brody_calibration.py tools/exp_metric_tensor_diagnostic.py tools/exp_modular_memory_spectrum.py tools/exp_two_channel_psd.py tools/exp_coherence_robustness.py tools/exp_coherence_length.py tools/exp_spectral_rigidity.py tools/dnd_zeros_vs_zeta.py tools/exp_boundary_shuffle_audit.py tools/exp_ricci_desitter_0406.py tools/data/exp_det_drift.json tools/exp_acf_stationarity.py tools/exp_det_drift.py tools/exp_spectral_2d.py tools/exp_spectral_landscape.py tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json tools/data/exp_markov_psd_prediction.json tools/data/prime_gaps_spectrum.png tools/data/exp_acf_stationarity.json tools/data/exp_conditional_r.json tools/data/exp_beta_crossover.json tools/data/tm1_figures/prime_gaps_spectrum_pub.png tools/data/trace_bridge_results.json tools/data/exp_two_channel_universality.json tools/data/denominator_gate_transfer_matrix.json tools/data/exp_two_channel_psd.json tools/data/exp_spectral_2d.json tools/data/exp_coherence_length.json tools/data/prime_gaps_spectrum_pub.png tools/data/psd_prime_gaps_results.json tools/data/exp_two_channel_decomposition.json tools/data/exp_acf_z6z_mechanism.json tools/data/exp_acf_range_universality.json tools/data/exp_poisson_convergence.json tools/data/semireal_order_denominator_gate_20260507_0923.json tools/data/reports/agent_20260507_0923.md tools/data/reports/exp_desitter_unification.json tools/data/reports/exp_metric_tensor_diag_long.json tools/data/reports/exp_dR_brody_connection.json tools/data/reports/exp_psd_prime_gaps.json tools/data/reports/exp_brody_crossover_20260405.json tools/data/reports/exp_crossover_universality.json tools/data/reports/exp_boundary_20260405_0825.json tools/data/reports/exp_boundary_growth_20260405_0914.json tools/data/reports/exp_excess_scaling_20260405.json tools/data/reports/exp_ricci_primes.json tools/data/reports/exp_number_variance_test.json tools/data/reports/exp_geodesic_deviation_primes.json tools/data/reports/exp_acf_decay_data.json tools/data/reports/agent_20260507_0901.md tools/data/lab_bridge_issues.jsonl tools/data/exp_psd_amp_scaling.json tools/data/exp_spectral_landscape.json tools/data/zeta_validation_results.json tools/data/spettro_zeta_results.json exec /bin/bash -lc "sed -n '1,240p' tools/data/reports/agent_20260507_0923.md 2>/dev/null || true" in /opt/MM_D-ND succeeded in 0ms: # Agent Report — Semi-Real Order Denominator Gate timestamp: 2026-05-07 09:23 UTC category: gate_falsification_semireal verdict: scoped_operator observables_registry: 1.0.0-2026-05-06 observables_used: [SR, SR2, L1, L2, triple_var] tool: tools/exp_semireal_order_denominator_gate.py data: tools/data/semireal_order_denominator_gate_20260507_0923.json seed_check: tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json ## Claim Under Test Verificato dal campo vivo: il cycle 0901 ha nominato `ORDER_DENOMINATOR_GATE` come supporto one-sided dell'ordine generato, ma lo ha fatto su perimetri sintetici. Il mandato corrente chiede falsificazione su domini non-sintetici o semi-reali. Domanda: se l'endpoint coerente è una sequenza ordinata reale/semi-reale e l'endpoint illusorio è una permutazione che preserva la marginale, il supporto denominatore resta one-sided o compare un controesempio? Perimetri: - `prime_gaps_first`: primi 4096 gap fra primi. - `zeta_zero_spacings_first`: primi 512 spacing fra zeri non banali di zeta, calcolati localmente con `mpmath.zetazero`. - `logistic_return_intervals`: 4096 intervalli di ritorno a `x > 0.95` nella mappa logistica caotica `x -> 4x(1-x)`. Gate: osservabile stabile se `abs(original - shuffle_mean) / shuffle_std >= 2`. ## Deposito Numerico Run principale: `n_replicates=20`, `n_beta=11`, `n_baseline=32`, `seed=202605070923`. Seed check: `n_replicates=12`, `n_baseline=24`, `seed=202605070924`. | perimeter | coherent one-sided observables | stable_count coherent | stable_count illusory | endpoint distance gated | beta ambiguous gated | |---|---:|---:|---:|---:|---:| | prime_gaps_first | SR, L1, triple_var | 3.000 | 0.650 | 3.270 | 0.30 | | logistic_return_intervals | [] | 0.200 | 0.100 | 0.000 | [] | | zeta_zero_spacings_first | SR, L2 | 2.150 | 0.250 | 2.666 | [] | Seed check: | perimeter | coherent one-sided observables | stable_count coherent | stable_count illusory | endpoint distance gated | beta ambiguous gated | |---|---:|---:|---:|---:|---:| | prime_gaps_first | SR, L1, triple_var | 3.000 | 0.250 | 3.288 | 0.30 | | logistic_return_intervals | [] | 0.000 | 0.583 | 0.000 | [] | | zeta_zero_spacings_first | SR, L2 | 2.417 | 0.333 | 2.700 | [] | Endpoint-stable observables: `[]` in all three perimeters in both runs. ## Risultato 1. **The order gate transfers to arithmetic and zeta spacing order.** Prime gaps carry one-sided support on `SR`, `L1`, and `triple_var`. Zeta-zero spacings carry one-sided support on `SR` and `L2`. In both cases the illusory endpoint remains weak-denominator under the same marginal. 2. **The logistic return perimeter is the counter-scope.** The logistic return sequence is ordered and generated by a deterministic chaotic system, but this canonical observable suite does not read its order as denominator support. The coherent endpoint stable count is `0.200` in the main run and `0.000` in the seed check. The gate does not transfer to this return-time observable. 3. **The transferable object is narrower than "real order".** `ORDER_DENOMINATOR_GATE` names order that survives a marginal-preserving shuffle in the canonical gap observables. It does not name every generated sequence. The node regressivo is the observable contract, not the gate threshold: if the order lives in return-time tail structure or symbolic itinerary, `SR/SR2/L1/L2/triple_var` can be blank. 4. **The beta layer is not universal.** Prime gaps reproduce beta `0.30` as the ambiguous protocol layer. Zeta has no gated ambiguous beta in this run. Logistic has no gated classifier because the one-sided observable set is empty. This extends 0901: beta `0.30` was a protocol fold in the synthetic matrix, not a cross-domain coordinate. ## Consecutio `ORDER_DENOMINATOR_GATE` survives as scoped operator: > In semi-real arithmetic/spectral spacing perimeters, the denominator gate is > one-sided support for order against a marginal-preserving shuffle. In > logistic return intervals, the canonical gap observables do not carry that > support; the gate output is blank rather than false-positive. Next experiment: do not tune `z_min`. Change the observable perimetro for the logistic counter-scope: symbolic itinerary block entropy, return-tail exponent, or recurrence-plot diagonal statistics, each with the same original-vs-shuffle denominator gate. That tests whether logistic order is absent for this gate or only invisible to the current canonical gap suite. ## Self-Audit: 5 Lenti L1 hard constraint vs bias: no universal claim is made. `endpoint_stable_observables: []` is exact for the run perimeters; "weak" means below the declared `abs(z)>=2` gate frequency, not numerical zero. L2 quantity vs ratio: raw stable counts and z means are reported before endpoint distances. No percentage drift claim is used. L3 no silent patching: 0901 claimed transfer on synthetic generated-order perimeters. This report narrows the scope after observing the logistic counter-perimeter; it does not rescue the original wording. L4 edge cases: logistic illusory stable_count is `0.583` in the seed check, so the claim is not "illusory endpoint absent." It is "no coherent one-sided support under the declared frequency rule." L5 re-discovery vs discovery: prime gap and zeta spacing order-vs-shuffle tests sit near known arithmetic/RMT order diagnostics; logistic return intervals sit near known recurrence and return-time diagnostics for chaotic maps. No NEW theorem is claimed. ## Fonti - Verificato: `tools/data/agent_field_live.md` - Verificato: `tools/LAB_AGENT_CONTEXT.md` - Verificato: `tools/observables_registry.py` - Verificato: `tools/exp_semireal_order_denominator_gate.py` - Verificato: `tools/data/semireal_order_denominator_gate_20260507_0923.json` - Verificato: `tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json` - Inferito: logistic return intervals are a counter-scope for the canonical gap-observable form of `ORDER_DENOMINATOR_GATE`. exec /bin/bash -lc "sed -n '1,260p' tools/observables_registry.py" in /opt/MM_D-ND succeeded in 0ms: """observables_registry.py — Source of Truth per gli observables del lab D-ND. Cristallizzato 2026-05-06 dalla **consecutio del cycle agent_20260506_0625**: > "What opens now: the lab needs an observable registry. Labels like SR > cannot travel between reports unless they bind to a function definition. > Without that, META flags are not philosophical: the same label can > silently change the object under test." ## Il problema che ha creato il registry Il cycle 06:25 ha auto-falsificato il finding del cycle 03:30 ("secondo asse GUE") e nel farlo ha trovato **collision di nomi observable** tra script: - `SR` in `exp_selective_layer_decoupling.py` = `spacing_ratio` (mean min/max ratio of consecutive gaps) — convention dominante (~6 script) - `SR` in `exp_scale_selective_perturbation.py` = `spectral_rigidity(gaps)` (Δ₃(L) rigidity) — variante usata SOLO in 1 script - `triple_var` in 3 script = `np.var(triple_sums)` (raw) — convention dominante - `triple_var` in `exp_perturbation_dimensionality_audit.py` = `np.var(triples) / np.var(gaps)` (normalizzato) — variante in 1 script Il lab autonomo che compara report tra script con osservabili "stesso nome, funzione diversa" stava confrontando mele con arance. ## La soluzione (minimal, non invasiva) Questo registry stabilisce il **nome canonico**: ciò che la maggioranza degli script chiama già `SR`/`triple_var`/etc. Le varianti restano disponibili ma con nomi ESPLICITI (`SR_local_rigidity`, `triple_var_normalized`) per evitare mascheramento semantico. ## Come usarlo ```python from observables_registry import OBSERVABLES_CANONICAL, OBSERVABLES_REGISTRY_VERSION # Compute canonical observable suite for a sequence of gaps results = {name: fn(gaps) for name, fn in OBSERVABLES_CANONICAL.items()} # Or import individual canonical observable from observables_registry import SR, triple_var, L1, L2, SR2 # For variants, import explicitly with disambiguating name from observables_registry import SR_local_rigidity, triple_var_normalized ``` ## Convention per i report Ogni report agent (cycle) che usa observables DEVE includere nel suo header: ``` observables_registry: 1.0.0-2026-05-06 observables_used: [SR, SR2, L1, L2, triple_var] ``` Cycle che mescola canonical + variant DEVE indicare entrambi: ``` observables_used: [SR, SR_local_rigidity, ...] ``` Senza questo, i confronti cross-cycle sono inattendibili. ## Versioning Cambiare una definizione canonica = bump del registry version e nota nel changelog. Le definizioni canoniche sono **immutabili dentro una versione**. """ from __future__ import annotations import numpy as np OBSERVABLES_REGISTRY_VERSION = "1.0.0-2026-05-06" # ─── Canonical observables (convention dominante nel codebase 2026-05-06) ─── def SR(gaps: np.ndarray) -> float: """**SR — Spacing Ratio** (canonical). Mean of `min(g_i, g_{i+1}) / max(g_i, g_{i+1})` over consecutive gaps. Range: (0, 1]. GUE → ~0.60. Poisson → ~0.39. Picket-fence → 1. NOTE: questa è la convention dominante in 6+ script del lab. Per la variante "local spectral rigidity Δ₃(L)" usare `SR_local_rigidity`. """ if len(gaps) < 2: return 0.0 s, s1 = gaps[:-1], gaps[1:] r = np.minimum(s, s1) / np.maximum(s, s1) r = r[np.isfinite(r) & (r > 0)] return float(np.mean(r)) if len(r) else 0.0 def SR2(gaps: np.ndarray) -> float: """**SR2 — Next-nearest Spacing Ratio** (canonical). Mean of `min(g_i, g_{i+2}) / max(g_i, g_{i+2})` skipping one gap. Probes lag-2 spacing structure. """ if len(gaps) < 3: return 0.0 s, s2 = gaps[:-2], gaps[2:] r = np.minimum(s, s2) / np.maximum(s, s2) r = r[np.isfinite(r) & (r > 0)] return float(np.mean(r)) if len(r) else 0.0 def L1(gaps: np.ndarray) -> float: """**L1 — Lag-1 Autocorrelation** (canonical). Standard ACF at lag 1 of the gap sequence. """ if len(gaps) < 3: return 0.0 g = gaps - np.mean(gaps) c0 = float(np.mean(g ** 2)) if c0 <= 1e-15: return 0.0 return float(np.mean(g[:-1] * g[1:]) / c0) def L2(gaps: np.ndarray) -> float: """**L2 — Lag-2 Autocorrelation** (canonical).""" if len(gaps) < 4: return 0.0 g = gaps - np.mean(gaps) c0 = float(np.mean(g ** 2)) if c0 <= 1e-15: return 0.0 return float(np.mean(g[:-2] * g[2:]) / c0) def triple_var(gaps: np.ndarray) -> float: """**triple_var — Variance of consecutive gap triples** (canonical). Variance of `g_i + g_{i+1} + g_{i+2}` over the sequence (RAW, no normalization). Convention used in 3+ scripts. For the normalized version (variance ratio `var(triples) / var(gaps)`) use `triple_var_normalized`. """ if len(gaps) < 3: return 0.0 t = gaps[:-2] + gaps[1:-1] + gaps[2:] return float(np.var(t)) # Set canonico per uso "compute all" da report OBSERVABLES_CANONICAL: dict[str, callable] = { "SR": SR, "SR2": SR2, "L1": L1, "L2": L2, "triple_var": triple_var, } # ─── Variants (esplicitamente nominate, no collision con canonical) ─── def SR_local_rigidity(gaps: np.ndarray, L: int = 10) -> float: """**SR_local_rigidity — Δ₃(L) Spectral Rigidity** (variant). Different observable than canonical `SR` (spacing ratio). Measures the average squared deviation of the cumulative spacing function from the best-fit straight line in a window of size L. Originated from `exp_scale_selective_perturbation.py` where it was locally named `SR` — registered here as `SR_local_rigidity` to avoid collision with canonical spacing-ratio definition. Use when explicitly studying spectral rigidity, NOT as alias for SR. """ if len(gaps) < 5: return 0.0 cumulative = np.cumsum(gaps) if cumulative[-1] <= 1e-15: return 0.0 cumulative = cumulative / cumulative[-1] * len(cumulative) n = np.arange(1, len(cumulative) + 1, dtype=float) window = int(min(L * len(gaps) / cumulative[-1], len(gaps) // 2)) if window < 5: return 0.0 residuals = [] for start in range(0, len(cumulative) - window, max(1, window // 2)): end = start + window x = n[start:end] y = cumulative[start:end] a, b = np.polyfit(x, y, 1) residuals.append(np.mean((y - (a * x + b)) ** 2)) return float(np.mean(residuals)) if residuals else 0.0 def triple_var_normalized(gaps: np.ndarray) -> float: """**triple_var_normalized — Variance of triples / variance of gaps** (variant). Originated from `exp_perturbation_dimensionality_audit.py` where it was locally named `triple_var` — registered here as `triple_var_normalized` to avoid collision with canonical raw triple variance. Use when explicitly studying triple-variance scaling relative to single-gap variance, NOT as alias for triple_var. """ if len(gaps) < 3: return 0.0 triples = gaps[:-2] + gaps[1:-1] + gaps[2:] v = float(np.var(gaps)) if v <= 1e-15: return 0.0 return float(np.var(triples) / v) # Set varianti, importabile esplicitamente OBSERVABLES_VARIANTS: dict[str, callable] = { "SR_local_rigidity": SR_local_rigidity, "triple_var_normalized": triple_var_normalized, } # ─── Public API ─────────────────────────────────────────────────────── def compute_canonical(gaps: np.ndarray) -> dict[str, float]: """Compute all canonical observables for a gap sequence. Returns dict {name: value} ready for inclusion in cycle reports. """ return {name: fn(gaps) for name, fn in OBSERVABLES_CANONICAL.items()} def report_header() -> str: """Suggested markdown header line for cycle reports using this registry.""" canonical_list = ", ".join(OBSERVABLES_CANONICAL.keys()) return ( f"observables_registry: {OBSERVABLES_REGISTRY_VERSION}\n" f"observables_used: [{canonical_list}]" ) __all__ = [ "OBSERVABLES_REGISTRY_VERSION", "OBSERVABLES_CANONICAL", "OBSERVABLES_VARIANTS", "SR", "SR2", "L1", "L2", "triple_var", "SR_local_rigidity", "triple_var_normalized", "compute_canonical", "report_header", ] if __name__ == "__main__": # Smoke test: canonical observables on a simple gap series rng = np.random.default_rng(42) gue_like = rng.gamma(shape=2.0, scale=0.5, size=200) exec /bin/bash -lc "sed -n '1,220p' tools/data/reports/agent_20260507_0901.md 2>/dev/null || true" in /opt/MM_D-ND succeeded in 0ms: # Agent Report — Denominator Gate Transfer Matrix timestamp: 2026-05-07 09:01 UTC category: gate_transferability_matrix verdict: operator observables_registry: 1.0.0-2026-05-06 observables_used: [SR, SR2, L1, L2, triple_var] tool: tools/exp_denominator_gate_transfer_matrix.py data: tools/data/denominator_gate_transfer_matrix.json ## Claim Under Test Verificato dal campo vivo: il cycle 0803 ha mostrato che il denominator gate trasferisce da BOUNDARY a DUALITA come operatore, ma non trasferisce la coordinata locale GUE/Poisson. Domanda di questo cycle: applicato a 4 perimetri non-BOUNDARY con poli coerente/illusorio a distribuzione marginale preservata, quali parti del gate trasferiscono e quali restano locali? Perimetri: - `DUALITA_golden`: sequenza Beatty aurea coerente vs permutazione illusoria. - `R_periodic_triad`: pattern periodico a 6 fasi vs permutazione illusoria. - `T_markov_alternating`: catena alternante low/high vs permutazione illusoria. - `E_ar1_continuity`: continuita AR(1) positiva vs permutazione illusoria. Parametri verificati: `n_gaps=4096`, `n_replicates=20`, `n_beta=11`, `n_baseline=32`, `z_min=2.0`, `seed=202605070901`. ## Deposito Numerico | perimeter | coherent one-sided observables | stable_count coherent | stable_count illusory | endpoint distance gated | ambiguous beta gated | |---|---:|---:|---:|---:|---:| | DUALITA_golden | SR, L1, triple_var | 3.00 | 0.25 | 3.418 | 0.30 | | R_periodic_triad | SR, SR2, L1, L2, triple_var | 5.00 | 0.25 | 4.400 | 0.30 | | T_markov_alternating | SR, SR2, L1, L2, triple_var | 5.00 | 0.05 | 4.412 | 0.30 | | E_ar1_continuity | SR, SR2, L1, L2, triple_var | 5.00 | 0.60 | 4.394 | 0.30 | Endpoint-stable observables: `[]` in all 4 perimeters. Verificato: no observable is stable at both coherent and illusory endpoints. The denominator gate is not a two-sided endpoint support. It is one-sided support for generated order. ## Risultato 1. The gate transfers as one-sided coherence support. In all 4 non-BOUNDARY perimeters, the coherent endpoint carries denominator support and the illusory endpoint loses it. `DUALITA_golden` transfers on `SR`, `L1`, `triple_var`; the three other perimeters transfer on all five canonical observables. 2. The both-endpoint stable set collapses everywhere. `endpoint_stable_observables: []` is not a failure of the test. It is the structural content: the gate does not name "two classes with stable denominators"; it names the side where order survives its own shuffle null. 3. The beta 0.30 ambiguity layer transfers as protocol coordinate, not as BOUNDARY coordinate. The ambiguous gated layer is beta `0.30` in all 4 perimeters. This is not a GUE/Poisson layer. It is the coordinate created by the replacement protocol: enough positions are illusory to place the layer near the endpoint bisector, while enough coherent order remains to keep large z support. 4. DUALITA is narrower than the other perimeters. `DUALITA_golden` has one-sided support on 3/5 observables. `SR2` and `L2` stay near shuffle at the coherent endpoint (`z_mean` about `0.12`). The DUALITA gate transfers, but only through lag-1 and triple aggregation in this synthetic perimeter. ## Consecutio The next word is not `boundary_layer`. The correct category is: `order_denominator_gate` Definition: an original-vs-shuffle denominator gate where support is expected on the coherent/generated side, endpoint-stable observables may be empty, and the layer coordinate belongs to the perturbation protocol unless anchored to a domain-specific semantic axis. This extends the 0803 result: - 0803: BOUNDARY denominator gate transfers to DUALITA, BOUNDARY coordinate does not. - 0901: across 4 non-BOUNDARY perimeters, the transferable object is one-sided order support; beta ambiguity is a protocol fold, not domain ontology. ## Self-Audit: 5 Lenti L1 hard constraint vs bias: no "always", "never", or "zero" claim is used without exact support. `endpoint_stable_observables: []` is exact for this run and perimeters. L2 quantity vs ratio: the report uses raw stable counts, endpoint distances, and beta layers. Percent language is avoided except where the script computes fractions. L3 no silent patching: the scope changed from BOUNDARY to non-BOUNDARY perimeters explicitly. The new category `order_denominator_gate` is declared instead of stretching `BOUNDARY_LAYER_GATE`. L4 edge cases: `E_ar1_continuity` has illusory stable_count `0.60`, so the illusory endpoint is weak-denominator, not denominator-absent. The claim is one-sided support dominance, not exact absence. L5 re-discovery vs discovery: the numerical mechanism is an original-vs-shuffle order test on synthetic sequences. No NEW mathematical theorem is claimed. ## Fonti - Verificato: `tools/data/agent_field_live.md` - Verificato: `tools/LAB_AGENT_CONTEXT.md` - Verificato: `tools/observables_registry.py` - Verificato: `tools/exp_denominator_gate_transfer_matrix.py` - Verificato: `tools/data/denominator_gate_transfer_matrix.json` - Inferito: `order_denominator_gate` as category needed by the downstream vocabulary, from the cross-perimeter matrix. codex Il cycle 09:23 ha già fatto primi, zeta e logistic return. Per non ripetere, cerco perimetri già presenti nel lab/sito che siano “ponte” fisico o semi-reale: metric tensor, geodesic deviation, PSD/acf, bridge traces. exec /bin/bash -lc "sed -n '1,260p' tools/exp_metric_tensor_diagnostic.py" in /opt/MM_D-ND succeeded in 0ms: #!/usr/bin/env python3 """ METRIC_TENSOR diagnostic — long experiment Piano 39, tensione METRIC_TENSOR (0.9) Domanda: DOVE vive la struttura dei primi nel tensore metrico g=(p/2)^2? - Curvatura scalare R => tautologica (z=-8.8, exp precedente) - Rapporti DeltaGamma => z=+22.5, ma non testato direttamente Esperimento: 1. Calcola i simboli di Christoffel Gamma^t_tt nella coordinata tau=ln(p) 2. Calcola DeltaGamma (variazione gap-to-gap della connessione) 3. Calcola rapporti DeltaGamma_n/DeltaGamma_{n+1} 4. Confronta con Cramer surrogates e shuffled gaps 5. Misura il contenuto spettrale di DeltaGamma vs dR 6. Cerca la firma di phi nei rapporti """ import json import numpy as np from datetime import datetime import sys np.random.seed(42) # ==== Generate primes via sieve ==== def sieve(limit): is_prime = np.ones(limit, dtype=bool) is_prime[:2] = False for i in range(2, int(limit**0.5)+1): if is_prime[i]: is_prime[i*i::i] = False return np.where(is_prime)[0] print("Generating primes up to 10^7...") primes = sieve(10_000_000) N = len(primes) print(f"N = {N} primes") # ==== Coordinate ==== p = primes.astype(np.float64) tau = np.log(p) # de Sitter time coordinate g = (p/2)**2 # metric tensor component # ==== Gaps ==== gaps = np.diff(p) log_gaps = np.diff(tau) # gaps in tau coordinate # ==== 1. Christoffel symbols ==== # For 1D metric g(tau), Gamma^tau_tautau = (1/2g) dg/dtau # In discrete: dg/dtau ~ (g[n+1]-g[n])/(tau[n+1]-tau[n]) dg = np.diff(g) dtau = np.diff(tau) g_mid = (g[:-1] + g[1:]) / 2 Gamma = dg / (2 * g_mid * dtau) print(f"Christoffel Gamma: mean={np.mean(Gamma):.6f}, std={np.std(Gamma):.6f}") # ==== 2. DeltaGamma ==== DeltaGamma = np.diff(Gamma) print(f"DeltaGamma: mean={np.mean(DeltaGamma):.6f}, std={np.std(DeltaGamma):.6f}") # ==== 3. Rapporti DeltaGamma consecutivi ==== # Evita divisione per zero mask = np.abs(DeltaGamma[:-1]) > 1e-20 DG_ratios = DeltaGamma[1:][mask] / DeltaGamma[:-1][mask] # Clamp outliers per statistiche robuste DG_ratios_clipped = np.clip(DG_ratios, -100, 100) print(f"DeltaGamma ratios: mean={np.mean(DG_ratios_clipped):.6f}, median={np.median(DG_ratios_clipped):.6f}") # ==== 4. Gap ratio (Oganesyan-Huse) per confronto ==== r_ratios = np.minimum(gaps[:-1], gaps[1:]) / np.maximum(gaps[:-1], gaps[1:]) r_mean_prime = np.mean(r_ratios) print(f"Gap ratio : {r_mean_prime:.6f}") # ==== 5. Curvature fluctuations dR ==== # R = 2 for de Sitter; dR = R_discrete - 2 # R_discrete from second derivative of g in tau d2g = np.diff(g, 2) dtau2 = dtau[:-1] * dtau[1:] # approximate g_center = g[1:-1] R_discrete = -d2g / (g_center * dtau2) + (dg[:-1]/(g_center*dtau[:-1]))**2 dR = R_discrete - 2.0 print(f"dR: mean={np.mean(dR):.6e}, std={np.std(dR):.6e}") # ==== 6. Null baselines ==== n_surr = 30 results_surr = { 'cramer': {'DG_std': [], 'DG_ratio_mean': [], 'DG_ratio_median': [], 'r_mean': [], 'dR_std': []}, 'shuffled': {'DG_std': [], 'DG_ratio_mean': [], 'DG_ratio_median': [], 'r_mean': [], 'dR_std': []} } print(f"Running {n_surr} surrogates each (Cramer + shuffled)...") for i in range(n_surr): # Cramer surrogate: gaps ~ Exponential(ln(p)) cramer_gaps = np.random.exponential(np.log(p[:len(gaps)]), size=len(gaps)) cramer_gaps = np.maximum(cramer_gaps, 2) # min gap = 2 cramer_p = np.cumsum(np.concatenate([[p[0]], cramer_gaps]))[:N] cramer_tau = np.log(np.maximum(cramer_p, 2)) cramer_g = (cramer_p/2)**2 cdg = np.diff(cramer_g) cdtau = np.diff(cramer_tau) cdtau[cdtau == 0] = 1e-15 cg_mid = (cramer_g[:-1] + cramer_g[1:]) / 2 cGamma = cdg / (2 * cg_mid * cdtau) cDG = np.diff(cGamma) cmask = np.abs(cDG[:-1]) > 1e-20 if np.sum(cmask) > 100: cDG_r = np.clip(cDG[1:][cmask] / cDG[:-1][cmask], -100, 100) results_surr['cramer']['DG_ratio_mean'].append(np.mean(cDG_r)) results_surr['cramer']['DG_ratio_median'].append(np.median(cDG_r)) results_surr['cramer']['DG_std'].append(np.std(cDG)) cr = np.minimum(cramer_gaps[:-1], cramer_gaps[1:]) / np.maximum(cramer_gaps[:-1], cramer_gaps[1:]) results_surr['cramer']['r_mean'].append(np.mean(cr)) # dR for Cramer cd2g = np.diff(cramer_g[:N], 2) cdtau2 = cdtau[:N-2] * cdtau[1:N-1] if len(cdtau) >= N-1 else cdtau[:-1]*cdtau[1:] min_len = min(len(cd2g), len(cdtau2)) cg_c = cramer_g[1:min_len+1] cR = -cd2g[:min_len] / (cg_c * cdtau2[:min_len]) + (cdg[:min_len]/(cg_c*cdtau[:min_len]))**2 results_surr['cramer']['dR_std'].append(np.std(cR - 2.0)) # Shuffled gaps shuf_gaps = np.random.permutation(gaps) shuf_p = np.cumsum(np.concatenate([[p[0]], shuf_gaps]))[:N] shuf_tau = np.log(np.maximum(shuf_p, 2)) shuf_g = (shuf_p/2)**2 sdg = np.diff(shuf_g) sdtau = np.diff(shuf_tau) sdtau[sdtau == 0] = 1e-15 sg_mid = (shuf_g[:-1] + shuf_g[1:]) / 2 sGamma = sdg / (2 * sg_mid * sdtau) sDG = np.diff(sGamma) smask = np.abs(sDG[:-1]) > 1e-20 if np.sum(smask) > 100: sDG_r = np.clip(sDG[1:][smask] / sDG[:-1][smask], -100, 100) results_surr['shuffled']['DG_ratio_mean'].append(np.mean(sDG_r)) results_surr['shuffled']['DG_ratio_median'].append(np.median(sDG_r)) results_surr['shuffled']['DG_std'].append(np.std(sDG)) sr = np.minimum(shuf_gaps[:-1], shuf_gaps[1:]) / np.maximum(shuf_gaps[:-1], shuf_gaps[1:]) results_surr['shuffled']['r_mean'].append(np.mean(sr)) sd2g = np.diff(shuf_g[:N], 2) sdtau2 = sdtau[:N-2] * sdtau[1:N-1] if len(sdtau) >= N-1 else sdtau[:-1]*sdtau[1:] min_len_s = min(len(sd2g), len(sdtau2)) sg_c = shuf_g[1:min_len_s+1] sR = -sd2g[:min_len_s] / (sg_c * sdtau2[:min_len_s]) + (sdg[:min_len_s]/(sg_c*sdtau[:min_len_s]))**2 results_surr['shuffled']['dR_std'].append(np.std(sR - 2.0)) print("Surrogates done.") # ==== 7. Z-scores ==== def zscore(val, surr_list): arr = np.array(surr_list) return (val - np.mean(arr)) / (np.std(arr) + 1e-30) z_DG_std_cramer = zscore(np.std(DeltaGamma), results_surr['cramer']['DG_std']) z_DG_std_shuffled = zscore(np.std(DeltaGamma), results_surr['shuffled']['DG_std']) z_r_cramer = zscore(r_mean_prime, results_surr['cramer']['r_mean']) z_r_shuffled = zscore(r_mean_prime, results_surr['shuffled']['r_mean']) z_dR_cramer = zscore(np.std(dR), results_surr['cramer']['dR_std']) z_dR_shuffled = zscore(np.std(dR), results_surr['shuffled']['dR_std']) if results_surr['cramer']['DG_ratio_median']: z_DGratio_cramer = zscore(np.median(DG_ratios_clipped), results_surr['cramer']['DG_ratio_median']) z_DGratio_shuffled = zscore(np.median(DG_ratios_clipped), results_surr['shuffled']['DG_ratio_median']) else: z_DGratio_cramer = z_DGratio_shuffled = float('nan') print(f"\n=== Z-SCORES ===") print(f"DeltaGamma std: z_cramer={z_DG_std_cramer:.2f}, z_shuffled={z_DG_std_shuffled:.2f}") print(f"DeltaGamma ratio median: z_cramer={z_DGratio_cramer:.2f}, z_shuffled={z_DGratio_shuffled:.2f}") print(f"Gap ratio : z_cramer={z_r_cramer:.2f}, z_shuffled={z_r_shuffled:.2f}") print(f"dR std: z_cramer={z_dR_cramer:.2f}, z_shuffled={z_dR_shuffled:.2f}") # ==== 8. Windowed analysis (scale dependence) ==== n_windows = 20 window_size = 20000 windows_data = [] indices = np.linspace(0, N - window_size - 3, n_windows, dtype=int) for idx in indices: w_p = p[idx:idx+window_size] w_tau = np.log(w_p) w_g = (w_p/2)**2 w_gaps = np.diff(w_p) # DeltaGamma in window wdg = np.diff(w_g) wdtau = np.diff(w_tau) wg_mid = (w_g[:-1] + w_g[1:])/2 wGamma = wdg / (2*wg_mid*wdtau) wDG = np.diff(wGamma) # DG ratios wmask = np.abs(wDG[:-1]) > 1e-20 if np.sum(wmask) > 10: wDG_r = np.clip(wDG[1:][wmask] / wDG[:-1][wmask], -100, 100) wDG_med = float(np.median(wDG_r)) else: wDG_med = float('nan') # gap ratio wr = np.minimum(w_gaps[:-1], w_gaps[1:]) / np.maximum(w_gaps[:-1], w_gaps[1:]) # dR wd2g = np.diff(w_g, 2) wdtau2 = wdtau[:-1]*wdtau[1:] wg_c = w_g[1:-1] wR = -wd2g / (wg_c * wdtau2) + (wdg[:-1]/(wg_c*wdtau[:-1]))**2 wdR = wR - 2.0 windows_data.append({ 'p_center': float(np.median(w_p)), 'ln_p': float(np.log(np.median(w_p))), 'DG_std': float(np.std(wDG)), 'DG_ratio_median': wDG_med, 'gap_r_mean': float(np.mean(wr)), 'dR_std': float(np.std(wdR)), 'dR_acf1': float(np.corrcoef(wdR[:-1], wdR[1:])[0,1]) if len(wdR)>2 else float('nan') }) # ==== 9. Correlation DG_ratio vs gap_r across windows ==== dg_meds = [w['DG_ratio_median'] for w in windows_data] gap_rs = [w['gap_r_mean'] for w in windows_data] valid = [i for i in range(len(dg_meds)) if not np.isnan(dg_meds[i])] if len(valid) > 5: corr_DGr_gapr = float(np.corrcoef([dg_meds[i] for i in valid], [gap_rs[i] for i in valid])[0,1]) else: corr_DGr_gapr = float('nan') print(f"\nCorrelation(DG_ratio_median, gap_r_mean) across windows: {corr_DGr_gapr:.4f}") # ==== 10. Spectral comparison: DeltaGamma vs dR ==== from numpy.fft import rfft # Use central 100K chunk for clean FFT chunk = 100000 start = N//2 - chunk//2 DG_chunk = DeltaGamma[start:start+chunk] dR_chunk = dR[start:start+chunk] min_chunk = min(len(DG_chunk), len(dR_chunk)) DG_chunk = DG_chunk[:min_chunk] dR_chunk = dR_chunk[:min_chunk] psd_DG = np.abs(rfft(DG_chunk - np.mean(DG_chunk)))**2 psd_dR = np.abs(rfft(dR_chunk - np.mean(dR_chunk)))**2 # Band power ratios (5 bands) n_fft = min(len(psd_DG), len(psd_dR)) bands = np.array_split(np.arange(1, n_fft), 5) band_ratio_DG_dR = [] exec /bin/bash -lc "sed -n '1,260p' tools/exp_geodesic_deviation_primes.py" in /opt/MM_D-ND succeeded in 0ms: """ Experiment: Geodesic deviation in prime metric g_n = (p_n/2)^2 After finding R=2 is tautological (holds for ANY monotonic sequence), we test what IS specific to primes. Key idea: The smooth Ricci scalar is trivially 2.0. What matters is the FLUCTUATION of the discrete curvature around 2.0. These fluctuations encode the gap structure of primes. We measure: 1. The spectrum of curvature fluctuations dR_n = R_n - 2 2. Autocorrelation of dR_n (does it inherit prime gap anti-correlation?) 3. Power spectral density of dR_n (prime-specific frequency structure?) 4. Compare ALL of these against shuffled-gap surrogates If primes show structure in dR_n that shuffled gaps don't, the metric captures something real about prime distribution, even though =2 is trivial. """ import numpy as np from sympy import primerange import json from datetime import datetime print("Generating primes up to 10^7...") primes = np.array(list(primerange(2, 10_000_000)), dtype=np.float64) N = len(primes) print(f"N = {N} primes") def compute_dR(seq): """Compute curvature fluctuations dR = R_n - 2 for a monotonic sequence.""" t = np.log(seq) a = seq / 2.0 dt = np.diff(t) dt_mid = (dt[:-1] + dt[1:]) / 2 da = np.diff(a) a_prime = da / dt da_prime = np.diff(a_prime) a_double_prime = da_prime / dt_mid R_n = 2.0 * a_double_prime / a[1:-1] return R_n - 2.0 def autocorr(x, max_lag=50): """Normalized autocorrelation.""" x = x - np.mean(x) n = len(x) var = np.var(x) if var == 0: return np.zeros(max_lag) result = np.correlate(x, x, mode='full') result = result[n-1:n-1+max_lag] / (var * n) return result # --- Prime curvature fluctuations --- dR_prime = compute_dR(primes) print(f"\nPrime dR fluctuations:") print(f" std(dR) = {np.std(dR_prime):.8f}") print(f" skew = {float(np.mean(dR_prime**3) / np.std(dR_prime)**3):.4f}") print(f" kurtosis= {float(np.mean(dR_prime**4) / np.std(dR_prime)**4 - 3):.4f}") # Autocorrelation of dR acf_prime = autocorr(dR_prime, max_lag=20) print(f"\n Autocorrelation of dR (prime):") for lag in [1, 2, 3, 5, 10]: print(f" lag {lag:>2}: {acf_prime[lag]:.6f}") # Power spectral density (first 10K points for speed) chunk = min(65536, len(dR_prime)) psd_prime = np.abs(np.fft.rfft(dR_prime[:chunk]))**2 / chunk freqs = np.fft.rfftfreq(chunk) # --- Shuffled-gap surrogates --- print(f"\n--- SURROGATES (20 shuffled-gap) ---") n_surr = 20 gaps = np.diff(primes) surr_stds = [] surr_acf1 = [] surr_acf2 = [] surr_psds = [] for s in range(n_surr): shuf_gaps = gaps.copy() np.random.shuffle(shuf_gaps) surr_seq = np.zeros(N) surr_seq[0] = primes[0] surr_seq[1:] = primes[0] + np.cumsum(shuf_gaps) dR_surr = compute_dR(surr_seq) surr_stds.append(np.std(dR_surr)) acf_surr = autocorr(dR_surr, max_lag=20) surr_acf1.append(acf_surr[1]) surr_acf2.append(acf_surr[2]) psd_surr = np.abs(np.fft.rfft(dR_surr[:chunk]))**2 / chunk surr_psds.append(psd_surr) surr_psd_mean = np.mean(surr_psds, axis=0) print(f" std(dR): prime={np.std(dR_prime):.8f}, surr={np.mean(surr_stds):.8f} +/- {np.std(surr_stds):.8f}") z_std = (np.std(dR_prime) - np.mean(surr_stds)) / np.std(surr_stds) if np.std(surr_stds) > 0 else 0 print(f" z-score(std): {z_std:.2f}") print(f"\n ACF lag-1: prime={acf_prime[1]:.6f}, surr={np.mean(surr_acf1):.6f} +/- {np.std(surr_acf1):.6f}") z_acf1 = (acf_prime[1] - np.mean(surr_acf1)) / np.std(surr_acf1) if np.std(surr_acf1) > 0 else 0 print(f" z-score(ACF1): {z_acf1:.2f}") print(f"\n ACF lag-2: prime={acf_prime[2]:.6f}, surr={np.mean(surr_acf2):.6f} +/- {np.std(surr_acf2):.6f}") z_acf2 = (acf_prime[2] - np.mean(surr_acf2)) / np.std(surr_acf2) if np.std(surr_acf2) > 0 else 0 print(f" z-score(ACF2): {z_acf2:.2f}") # PSD ratio in bands n_bands = 5 band_size = len(freqs) // n_bands print(f"\n PSD ratio (prime/surrogate) by frequency band:") psd_ratios = [] for b in range(n_bands): s_idx = b * band_size + 1 # skip DC e_idx = (b + 1) * band_size ratio = np.mean(psd_prime[s_idx:e_idx]) / np.mean(surr_psd_mean[s_idx:e_idx]) freq_range = f"[{freqs[s_idx]:.4f}, {freqs[e_idx-1]:.4f}]" print(f" band {b+1} {freq_range}: ratio = {ratio:.4f}") psd_ratios.append(round(ratio, 4)) # --- Cramer surrogates (model comparison) --- print(f"\n--- CRAMER SURROGATES (random model primes) ---") cramer_stds = [] cramer_acf1 = [] for s in range(n_surr): # Cramer model: gap ~ exponential with rate 1/ln(p) cramer_seq = [primes[0]] for i in range(1, N): gap = max(2, round(np.random.exponential(np.log(cramer_seq[-1])) / 2) * 2) cramer_seq.append(cramer_seq[-1] + gap) cramer_seq = np.array(cramer_seq, dtype=np.float64) dR_cramer = compute_dR(cramer_seq) cramer_stds.append(np.std(dR_cramer)) acf_cramer = autocorr(dR_cramer, max_lag=5) cramer_acf1.append(acf_cramer[1]) print(f" std(dR): prime={np.std(dR_prime):.8f}, Cramer={np.mean(cramer_stds):.8f} +/- {np.std(cramer_stds):.8f}") z_cramer_std = (np.std(dR_prime) - np.mean(cramer_stds)) / np.std(cramer_stds) if np.std(cramer_stds) > 0 else 0 print(f" z-score(std vs Cramer): {z_cramer_std:.2f}") print(f"\n ACF lag-1: prime={acf_prime[1]:.6f}, Cramer={np.mean(cramer_acf1):.6f} +/- {np.std(cramer_acf1):.6f}") z_cramer_acf = (acf_prime[1] - np.mean(cramer_acf1)) / np.std(cramer_acf1) if np.std(cramer_acf1) > 0 else 0 print(f" z-score(ACF1 vs Cramer): {z_cramer_acf:.2f}") # --- Summary --- print(f"\n{'='*60}") print("SUMMARY:") print(f" 1. = 2.0 is TAUTOLOGICAL (holds for any sequence)") print(f" 2. The FLUCTUATIONS dR = R-2 encode gap structure") print(f" 3. Prime dR vs shuffled:") print(f" - std: z = {z_std:.1f} ({'DIFFERENT' if abs(z_std) > 2 else 'SAME'})") print(f" - ACF1: z = {z_acf1:.1f} ({'DIFFERENT' if abs(z_acf1) > 2 else 'SAME'})") print(f" - ACF2: z = {z_acf2:.1f} ({'DIFFERENT' if abs(z_acf2) > 2 else 'SAME'})") print(f" 4. Prime dR vs Cramer:") print(f" - std: z = {z_cramer_std:.1f}") print(f" - ACF1: z = {z_cramer_acf:.1f}") verdict = "CONSTRAINT" finding = "R=2 tautological. Curvature fluctuations encode gap correlations." if abs(z_acf1) > 3 and abs(z_cramer_acf) > 3: verdict = "NEW" finding += " Prime-specific autocorrelation in dR detected vs both baselines." elif abs(z_acf1) > 3: finding += " dR autocorrelation differs from shuffled but similar to Cramer." print(f"\nVERDICT: {verdict}") print(f"FINDING: {finding}") # Save results = { "experiment": "exp_geodesic_deviation_primes", "timestamp": datetime.now().isoformat(), "piano": 39, "tension": "METRIC_TENSOR", "claim_tested": "g=(p/2)^2 de Sitter — is R=2 specific to primes?", "answer": "NO. R=2 is tautological. The FLUCTUATIONS dR=R-2 encode prime structure.", "N_primes": N, "prime_dR_std": round(float(np.std(dR_prime)), 8), "prime_acf_lag1": round(float(acf_prime[1]), 6), "prime_acf_lag2": round(float(acf_prime[2]), 6), "shuffled_baseline": { "n_surrogates": n_surr, "dR_std_mean": round(float(np.mean(surr_stds)), 8), "z_score_std": round(z_std, 2), "z_score_acf1": round(z_acf1, 2), "z_score_acf2": round(z_acf2, 2) }, "cramer_baseline": { "n_surrogates": n_surr, "dR_std_mean": round(float(np.mean(cramer_stds)), 8), "z_score_std": round(z_cramer_std, 2), "z_score_acf1": round(z_cramer_acf, 2) }, "psd_ratios_by_band": psd_ratios, "verdict": verdict, "finding": finding } with open("/opt/MM_D-ND/tools/data/reports/exp_geodesic_deviation_primes.json", "w") as f: json.dump(results, f, indent=2) print(f"\nSaved to data/reports/exp_geodesic_deviation_primes.json") exec /bin/bash -lc "sed -n '1,260p' tools/exp_semireal_order_denominator_gate.py" in /opt/MM_D-ND succeeded in 0ms: #!/usr/bin/env python3 """ exp_semireal_order_denominator_gate.py Falsification attempt for ORDER_DENOMINATOR_GATE on non-synthetic / semi-real ordered sequences. The coherent endpoint is the observed order of each sequence; the illusory endpoint is a marginal-preserving permutation. The same original-vs-shuffle denominator gate used in the prior reports is applied to canonical observables from observables_registry.py. """ from __future__ import annotations import argparse import json import math from pathlib import Path import numpy as np from observables_registry import ( OBSERVABLES_CANONICAL, OBSERVABLES_REGISTRY_VERSION, compute_canonical, ) OBS_NAMES = list(OBSERVABLES_CANONICAL.keys()) def normalize(gaps: np.ndarray) -> np.ndarray: gaps = np.asarray(gaps, dtype=float) gaps = np.maximum(gaps, 1e-12) mean = float(np.mean(gaps)) return gaps / mean if mean > 1e-15 else gaps def sieve_primes_for_count(n_primes: int) -> np.ndarray: if n_primes < 6: limit = 20 else: limit = int(n_primes * (math.log(n_primes) + math.log(math.log(n_primes))) * 1.25) while True: sieve = np.ones(limit + 1, dtype=bool) sieve[:2] = False for p in range(2, int(limit**0.5) + 1): if sieve[p]: sieve[p * p : limit + 1 : p] = False primes = np.flatnonzero(sieve) if len(primes) >= n_primes: return primes[:n_primes].astype(float) limit *= 2 def prime_gap_sequence(n_gaps: int) -> np.ndarray: primes = sieve_primes_for_count(n_gaps + 1) return normalize(np.diff(primes)) def zeta_zero_spacings(n_gaps: int) -> np.ndarray: try: import mpmath as mp except ImportError as exc: raise RuntimeError("mpmath is required for zeta_zero_spacings") from exc zeros = np.empty(n_gaps + 1, dtype=float) for i in range(n_gaps + 1): zeros[i] = float(mp.im(mp.zetazero(i + 1))) return normalize(np.diff(zeros)) def logistic_return_intervals(n_gaps: int, rng: np.random.Generator) -> np.ndarray: # Return intervals to a high-density-edge event in the fully chaotic logistic map. threshold = 0.95 burn = 2000 needed = n_gaps + 1 returns: list[int] = [] last_hit: int | None = None x = float(rng.random()) i = 0 max_steps = 50_000_000 while len(returns) < needed and i < max_steps: x = 4.0 * x * (1.0 - x) if i >= burn and x > threshold: if last_hit is not None: returns.append(i - last_hit) last_hit = i i += 1 if len(returns) < needed: raise RuntimeError(f"logistic generator produced {len(returns)} intervals, need {needed}") return normalize(np.array(returns[:n_gaps], dtype=float)) def beta_replace(base: np.ndarray, beta: float, rng: np.random.Generator) -> np.ndarray: illusory = rng.permutation(base) if beta <= 0.0: return base.copy() if beta >= 1.0: return illusory out = base.copy() mask = rng.random(len(base)) < beta out[mask] = illusory[mask] return normalize(out) def z_against_shuffle( gaps: np.ndarray, n_baseline: int, rng: np.random.Generator, ) -> tuple[dict[str, float], dict[str, float], dict[str, float], dict[str, float]]: original = compute_canonical(gaps) baseline = {name: [] for name in OBS_NAMES} for _ in range(n_baseline): obs = compute_canonical(rng.permutation(gaps)) for name in OBS_NAMES: baseline[name].append(obs[name]) means = {} sds = {} z = {} for name in OBS_NAMES: vals = np.array(baseline[name], dtype=float) means[name] = float(np.mean(vals)) sds[name] = float(np.std(vals, ddof=1)) if len(vals) > 1 else 0.0 z[name] = float((original[name] - means[name]) / sds[name]) if sds[name] > 1e-15 else 0.0 return original, means, sds, z def vector(row: dict, names: list[str]) -> np.ndarray: return np.array([row["observables"][name] for name in names], dtype=float) def classify_layers(rows: list[dict], obs_names: list[str]) -> dict: if not obs_names: return {"observables": [], "endpoint_distance": 0.0, "layers": {}, "ambiguous_beta": []} by_beta: dict[float, list[dict]] = {} for row in rows: by_beta.setdefault(float(row["beta"]), []).append(row) coherent = np.array([vector(row, obs_names) for row in by_beta[0.0]], dtype=float) illusory = np.array([vector(row, obs_names) for row in by_beta[1.0]], dtype=float) endpoints = np.vstack([coherent, illusory]) scale = np.std(endpoints, axis=0, ddof=1) scale[scale <= 1e-15] = 1.0 coherent_centroid = np.mean(coherent, axis=0) illusory_centroid = np.mean(illusory, axis=0) endpoint_distance = float(np.linalg.norm((illusory_centroid - coherent_centroid) / scale)) layers = {} ambiguous_beta = [] for beta, beta_rows in sorted(by_beta.items()): margins = [] labels = [] coords = [] for row in beta_rows: x = vector(row, obs_names) d_coherent = float(np.linalg.norm((x - coherent_centroid) / scale)) d_illusory = float(np.linalg.norm((x - illusory_centroid) / scale)) denom = d_coherent + d_illusory coord = float((d_coherent - d_illusory) / denom) if denom > 1e-15 else 0.0 margin = float(abs(d_coherent - d_illusory) / denom) if denom > 1e-15 else 0.0 coords.append(coord) margins.append(margin) labels.append("coherent" if d_coherent < d_illusory else "illusory") ambiguous_fraction = float(np.mean(np.array(margins) < 0.15)) if ambiguous_fraction >= 0.5: ambiguous_beta.append(beta) layers[f"{beta:.3f}"] = { "coordinate_mean": float(np.mean(coords)), "margin_mean": float(np.mean(margins)), "ambiguous_fraction": ambiguous_fraction, "illusory_label_fraction": float(np.mean(np.array(labels) == "illusory")), } return { "observables": obs_names, "endpoint_distance": endpoint_distance, "layers": layers, "ambiguous_beta": ambiguous_beta, } def summarize_gate(rows: list[dict], z_min: float) -> dict: by_beta: dict[float, list[dict]] = {} for row in rows: by_beta.setdefault(float(row["beta"]), []).append(row) layers = {} for beta, beta_rows in sorted(by_beta.items()): stable_counts = [] stable_freq = {name: [] for name in OBS_NAMES} z_values = {name: [] for name in OBS_NAMES} for row in beta_rows: stable = [name for name in OBS_NAMES if abs(row["z"][name]) >= z_min] stable_counts.append(len(stable)) for name in OBS_NAMES: stable_freq[name].append(1.0 if name in stable else 0.0) z_values[name].append(row["z"][name]) layers[f"{beta:.3f}"] = { "stable_count_mean": float(np.mean(stable_counts)), "stable_frequency": {name: float(np.mean(vals)) for name, vals in stable_freq.items()}, "z_mean": {name: float(np.mean(vals)) for name, vals in z_values.items()}, } one_sided = [] endpoint_stable = [] coherent_rows = by_beta[0.0] illusory_rows = by_beta[1.0] for name in OBS_NAMES: coherent_freq = float(np.mean([abs(row["z"][name]) >= z_min for row in coherent_rows])) illusory_freq = float(np.mean([abs(row["z"][name]) >= z_min for row in illusory_rows])) if coherent_freq >= 0.75 and illusory_freq < 0.25: one_sided.append(name) if coherent_freq >= 0.75 and illusory_freq >= 0.75: endpoint_stable.append(name) return { "z_min": z_min, "coherent_one_sided_observables": one_sided, "endpoint_stable_observables": endpoint_stable, "layers": layers, } def analyze_sequence(name: str, base: np.ndarray, args: argparse.Namespace, rng: np.random.Generator) -> dict: rows = [] betas = [float(x) for x in np.linspace(0.0, 1.0, args.n_beta)] for rep in range(args.n_replicates): rep_rng = np.random.default_rng(rng.integers(0, 2**63 - 1)) for beta in betas: gaps = beta_replace(base, beta, rep_rng) obs, shuffle_mean, shuffle_std, z = z_against_shuffle( gaps, args.n_baseline, np.random.default_rng(rng.integers(0, 2**63 - 1)), ) rows.append( { "perimeter": name, "replicate": rep, "beta": beta, "observables": obs, "shuffle_mean": shuffle_mean, "shuffle_std": shuffle_std, "z": z, "stable_observables": [obs_name for obs_name in OBS_NAMES if abs(z[obs_name]) >= args.z_min], } ) gate = summarize_gate(rows, args.z_min) return { "source": { "n_gaps": int(len(base)), "mean": float(np.mean(base)), "variance": float(np.var(base)), }, "gate": gate, "classification_all_observables": classify_layers(rows, OBS_NAMES), "classification_one_sided_gated": classify_layers(rows, gate["coherent_one_sided_observables"]), exec /bin/bash -lc "sed -n '1,260p' tools/dnd_trace_bridge.py" in /opt/MM_D-ND succeeded in 0ms: #!/usr/bin/env python3 """ Piano 14 — Trace Bridge Experiment (v2) Ogni punto di partenza x₀ "misura" φ attraverso la mappa f(x)=1+1/x. La convergenza: x_n = φ + (x₀-φ)·(-1/φ²)^n + O((x₀-φ)²·(1/φ⁴)^n) Linearizzazione: f'(φ) = -1/φ² ≈ -0.382 - Tasso di convergenza: 1/φ² (invariante della mappa) - Segno alterna: la simmetria di Galois σ: φ → -1/φ L'inversa f⁻¹(x) = 1/(x-1) ha f⁻¹'(φ) = -φ² (instabile, amplifica) - L'inversa RIVELA la struttura perché amplifica le differenze Osservazione chiave dell'operatore: "Ogni misura della stessa costante deve essere su una curva ma spostata" → La curva è la traiettoria x_n(x₀). Lo shift è x₀. → L'osservabile non è A (tautologico: A=x₀-φ) ma la CURVATURA della traiettoria. """ import numpy as np import json from pathlib import Path PHI = (1 + np.sqrt(5)) / 2 LAMBDA = -1.0 / PHI**2 # eigenvalue: f'(φ) ≈ -0.382 DATA_DIR = Path(__file__).parent / "data" def dnd_map(x): """f(x) = 1 + 1/x""" if abs(x) < 1e-15: return float('inf') return 1.0 + 1.0 / x def dnd_inverse(x): """f⁻¹(x) = 1/(x-1)""" if abs(x - 1.0) < 1e-15: return float('inf') return 1.0 / (x - 1.0) def trajectory(x0, n_iter=30, forward=True): """Genera la traiettoria completa.""" f = dnd_map if forward else dnd_inverse traj = [x0] x = x0 for _ in range(n_iter): x = f(x) if not np.isfinite(x) or abs(x) > 1e15: break traj.append(x) return np.array(traj) def curvature_discrete(traj): """ Curvatura discreta della traiettoria nel piano (n, x_n). K_n = |x_{n-1} - 2x_n + x_{n+1}| / (1 + ((x_{n+1}-x_{n-1})/2)²)^(3/2) """ if len(traj) < 3: return np.array([]) K = [] for i in range(1, len(traj) - 1): num = abs(traj[i-1] - 2*traj[i] + traj[i+1]) slope = (traj[i+1] - traj[i-1]) / 2.0 denom = (1 + slope**2) ** 1.5 K.append(num / denom if denom > 1e-30 else 0.0) return np.array(K) def nonlinear_residual(x0, n_iter=25): """ La parte NON-LINEARE della convergenza. Linearizzato: x_n^lin = φ + (x₀-φ)·λ^n Residuo: r_n = x_n - x_n^lin Il residuo contiene l'informazione non-tautologica. """ traj = trajectory(x0, n_iter, forward=True) delta0 = x0 - PHI residuals = [] for n in range(len(traj)): x_linear = PHI + delta0 * LAMBDA**n r = traj[n] - x_linear residuals.append(r) return np.array(residuals) def load_zeta_zeros(n_zeros=2000): """Carica i primi n_zeros zeri di ζ.""" zeros_file = DATA_DIR / "odlyzko_cache" / "zeros1.txt" if not zeros_file.exists(): print(f"ERRORE: {zeros_file} non trovato") return None zeros = [] with open(zeros_file) as f: for line in f: line = line.strip() if line: try: zeros.append(float(line)) except ValueError: continue if len(zeros) >= n_zeros: break return np.array(zeros) def spacing_stats(values): """Statistiche degli spacing normalizzati.""" values = np.sort(values[np.isfinite(values)]) if len(values) < 5: return None spacings = np.diff(values) spacings = spacings[spacings > 0] if len(spacings) < 3: return None mean_s = np.mean(spacings) if mean_s < 1e-30: return None s = spacings / mean_s ratios = [] for i in range(len(s) - 1): mx = max(s[i], s[i+1]) if mx > 0: ratios.append(min(s[i], s[i+1]) / mx) return { "mean_r": float(np.mean(ratios)) if ratios else 0, "var_s": float(np.var(s)), "n": int(len(s)), "label_GUE": "=0.5996, Var=0.178", "label_Poi": "=0.3863, Var=1.0", } def print_stats(name, stats): if stats is None: print(f" {name}: dati insufficienti") return d_gue = abs(stats['mean_r'] - 0.5996) d_poi = abs(stats['mean_r'] - 0.3863) closer = "GUE" if d_gue < d_poi else "Poisson" print(f" {name} (N={stats['n']}): ={stats['mean_r']:.4f} Var(s)={stats['var_s']:.3f} → {closer}") def run(): print("=" * 65) print("PIANO 14 — TRACE BRIDGE v2") print(f"λ = f'(φ) = -1/φ² = {LAMBDA:.6f}") print("=" * 65) zeros = load_zeta_zeros(2000) if zeros is None: return print(f"Zeri ζ caricati: {len(zeros)}") np.random.seed(42) random_pts = np.sort(np.random.uniform(zeros[0], zeros[-1], len(zeros))) uniform_pts = np.linspace(zeros[0], zeros[-1], len(zeros)) results = {} # ================================================================ # ESPERIMENTO 1: Curvatura delle traiettorie forward # ================================================================ print("\n--- ESPERIMENTO 1: Curvatura traiettorie forward ---") print("Ogni x₀ genera una traiettoria → calcoliamo K (curvatura discreta)") def curvatures_at_step(points, step=3, n_iter=15): """Curvatura al passo `step` per ogni punto di partenza.""" Ks = [] for x0 in points: traj = trajectory(x0, n_iter, forward=True) K = curvature_discrete(traj) if len(K) > step: Ks.append(K[step]) return np.array(Ks) for step in [2, 3, 5, 8]: print(f"\n Step {step}:") K_zeta = curvatures_at_step(zeros, step) K_rand = curvatures_at_step(random_pts, step) K_unif = curvatures_at_step(uniform_pts, step) s_z = spacing_stats(K_zeta) s_r = spacing_stats(K_rand) s_u = spacing_stats(K_unif) print_stats("zeta", s_z) print_stats("random", s_r) print_stats("uniform", s_u) if s_z: results[f"curvature_step{step}_zeta"] = s_z if s_r: results[f"curvature_step{step}_random"] = s_r # ================================================================ # ESPERIMENTO 2: Residuo non-lineare # ================================================================ print("\n--- ESPERIMENTO 2: Residuo non-lineare ---") print("r_n = x_n - [φ + (x₀-φ)·λ^n] — la parte oltre la linearizzazione") def residual_at_step(points, step=5): Rs = [] for x0 in points: r = nonlinear_residual(x0, n_iter=max(step+2, 15)) if len(r) > step: Rs.append(r[step]) return np.array(Rs) for step in [3, 5, 8, 12]: print(f"\n Step {step}:") R_zeta = residual_at_step(zeros, step) R_rand = residual_at_step(random_pts, step) s_z = spacing_stats(R_zeta) s_r = spacing_stats(R_rand) print_stats("zeta", s_z) print_stats("random", s_r) if s_z: results[f"residual_step{step}_zeta"] = s_z if s_r: results[f"residual_step{step}_random"] = s_r # ================================================================ # ESPERIMENTO 3: Mappa inversa (amplifica la struttura) # ================================================================ print("\n--- ESPERIMENTO 3: Mappa inversa ---") print("f⁻¹(x) = 1/(x-1), f⁻¹'(φ) = -φ² ≈ -2.618 (instabile)") def inverse_trajectory_lengths(points, threshold=1e6, n_iter=30): """Quanti passi prima di divergere oltre threshold.""" lengths = [] for x0 in points: traj = trajectory(x0, n_iter, forward=False) # Trova quando diverge for i, x in enumerate(traj): if abs(x) > threshold or not np.isfinite(x): lengths.append(i) break else: lengths.append(len(traj)) return np.array(lengths, dtype=float) # Curvatura della traiettoria inversa ai primi passi for step in [1, 2, 3]: print(f"\n Step {step} (inversa):") K_inv_zeta = [] K_inv_rand = [] for x0 in zeros[:1000]: traj = trajectory(x0, 10, forward=False) K = curvature_discrete(traj) exec /bin/bash -lc "sed -n '260,560p' tools/exp_semireal_order_denominator_gate.py" in /opt/MM_D-ND succeeded in 0ms: "classification_one_sided_gated": classify_layers(rows, gate["coherent_one_sided_observables"]), "rows": rows, } def build_sequences(args: argparse.Namespace, rng: np.random.Generator) -> dict[str, np.ndarray]: sequences = { "prime_gaps_first": prime_gap_sequence(args.n_gaps), "logistic_return_intervals": logistic_return_intervals(args.n_gaps, rng), } if args.include_zeta: sequences["zeta_zero_spacings_first"] = zeta_zero_spacings(args.zeta_gaps) return sequences def compact(perimeters: dict) -> dict: out = {} for name, data in perimeters.items(): gate = data["gate"] all_cls = data["classification_all_observables"] gated_cls = data["classification_one_sided_gated"] out[name] = { "n_gaps": data["source"]["n_gaps"], "coherent_one_sided_observables": gate["coherent_one_sided_observables"], "endpoint_stable_observables": gate["endpoint_stable_observables"], "stable_count_coherent": gate["layers"]["0.000"]["stable_count_mean"], "stable_count_illusory": gate["layers"]["1.000"]["stable_count_mean"], "z_mean_coherent": gate["layers"]["0.000"]["z_mean"], "z_mean_illusory": gate["layers"]["1.000"]["z_mean"], "endpoint_distance_all": all_cls["endpoint_distance"], "endpoint_distance_one_sided_gated": gated_cls["endpoint_distance"], "ambiguous_beta_one_sided_gated": gated_cls["ambiguous_beta"], } return out def run(args: argparse.Namespace) -> dict: root_rng = np.random.default_rng(args.seed) sequences = build_sequences(args, root_rng) perimeters = {} for name, base in sequences.items(): perimeters[name] = analyze_sequence(name, base, args, root_rng) output = { "experiment": "semireal_order_denominator_gate", "category": "gate_falsification_semireal", "question": "Does ORDER_DENOMINATOR_GATE survive on non-synthetic / semi-real ordered sequences?", "observables_registry": OBSERVABLES_REGISTRY_VERSION, "observables_used": OBS_NAMES, "params": vars(args), "matrix": compact(perimeters), "perimeters": perimeters, } out_path = Path(args.out) out_path.parent.mkdir(parents=True, exist_ok=True) with out_path.open("w") as f: json.dump(output, f, indent=2) print(f"observables_registry={OBSERVABLES_REGISTRY_VERSION}") print(f"observables_used={OBS_NAMES}") print("perimeter n one_sided stable0 stable1 dist_gate ambiguous_gate") for name, row in output["matrix"].items(): print( f"{name:>28s} " f"{row['n_gaps']:>5d} " f"{','.join(row['coherent_one_sided_observables']) or '[]':>22s} " f"{row['stable_count_coherent']:>7.3f} " f"{row['stable_count_illusory']:>7.3f} " f"{row['endpoint_distance_one_sided_gated']:>9.3f} " f"{row['ambiguous_beta_one_sided_gated']}" ) print(f"saved {out_path}") return output def main() -> None: parser = argparse.ArgumentParser() parser.add_argument("--n-gaps", type=int, default=4096) parser.add_argument("--zeta-gaps", type=int, default=1024) parser.add_argument("--include-zeta", action="store_true") parser.add_argument("--n-replicates", type=int, default=20) parser.add_argument("--n-beta", type=int, default=11) parser.add_argument("--n-baseline", type=int, default=32) parser.add_argument("--z-min", type=float, default=2.0) parser.add_argument("--seed", type=int, default=202605070923) parser.add_argument("--out", default="tools/data/semireal_order_denominator_gate_20260507_0923.json") args = parser.parse_args() run(args) if __name__ == "__main__": main() exec /bin/bash -lc "sed -n '1,240p' tools/data/trace_bridge_results.json 2>/dev/null || true" in /opt/MM_D-ND succeeded in 0ms: { "curvature_step2_zeta": { "mean_r": 0.6170613873912985, "var_s": 266.24942353150647, "n": 1999, "label_GUE": "=0.5996, Var=0.178", "label_Poi": "=0.3863, Var=1.0" }, "curvature_step2_random": { "mean_r": 0.3959342658083805, "var_s": 70.59851830290162, "n": 1999, "label_GUE": "=0.5996, Var=0.178", "label_Poi": "=0.3863, Var=1.0" }, "curvature_step3_zeta": { "mean_r": 0.6170623535001659, "var_s": 264.95845602931996, "n": 1999, "label_GUE": "=0.5996, Var=0.178", "label_Poi": "=0.3863, Var=1.0" }, "curvature_step3_random": { "mean_r": 0.3959342507572091, "var_s": 70.32881904106682, "n": 1999, "label_GUE": "=0.5996, Var=0.178", "label_Poi": "=0.3863, Var=1.0" }, "curvature_step5_zeta": { "mean_r": 0.6170631413021277, "var_s": 263.9572348011863, "n": 1999, "label_GUE": "=0.5996, Var=0.178", "label_Poi": "=0.3863, Var=1.0" }, "curvature_step5_random": { "mean_r": 0.39593425705520185, "var_s": 70.15134555544962, "n": 1999, "label_GUE": "=0.5996, Var=0.178", "label_Poi": "=0.3863, Var=1.0" }, "curvature_step8_zeta": { "mean_r": 0.6170632649432327, "var_s": 263.8112087964937, "n": 1999, "label_GUE": "=0.5996, Var=0.178", "label_Poi": "=0.3863, Var=1.0" }, "curvature_step8_random": { "mean_r": 0.3959340783831598, "var_s": 70.12553054603664, "n": 1999, "label_GUE": "=0.5996, Var=0.178", "label_Poi": "=0.3863, Var=1.0" }, "residual_step3_zeta": { "mean_r": 0.6171751538918284, "var_s": 0.21085749119231775, "n": 1999, "label_GUE": "=0.5996, Var=0.178", "label_Poi": "=0.3863, Var=1.0" }, "residual_step3_random": { "mean_r": 0.39586458066360825, "var_s": 0.994110937474891, "n": 1999, "label_GUE": "=0.5996, Var=0.178", "label_Poi": "=0.3863, Var=1.0" }, "residual_step5_zeta": { "mean_r": 0.6171752482714437, "var_s": 0.21080653697683885, "n": 1999, "label_GUE": "=0.5996, Var=0.178", "label_Poi": "=0.3863, Var=1.0" }, "residual_step5_random": { "mean_r": 0.3958645691697114, "var_s": 0.9941135045572564, "n": 1999, "label_GUE": "=0.5996, Var=0.178", "label_Poi": "=0.3863, Var=1.0" }, "residual_step8_zeta": { "mean_r": 0.6171752664980846, "var_s": 0.21079664958486305, "n": 1999, "label_GUE": "=0.5996, Var=0.178", "label_Poi": "=0.3863, Var=1.0" }, "residual_step8_random": { "mean_r": 0.3958645669268979, "var_s": 0.9941140061742251, "n": 1999, "label_GUE": "=0.5996, Var=0.178", "label_Poi": "=0.3863, Var=1.0" }, "residual_step12_zeta": { "mean_r": 0.6171752655471571, "var_s": 0.21079716597090106, "n": 1999, "label_GUE": "=0.5996, Var=0.178", "label_Poi": "=0.3863, Var=1.0" }, "residual_step12_random": { "mean_r": 0.3958645670465852, "var_s": 0.9941139799479727, "n": 1999, "label_GUE": "=0.5996, Var=0.178", "label_Poi": "=0.3863, Var=1.0" }, "inverse_curv_step1_zeta": { "mean_r": 0.6167411436973637, "var_s": 155.0061224954674, "n": 999, "label_GUE": "=0.5996, Var=0.178", "label_Poi": "=0.3863, Var=1.0" }, "inverse_curv_step1_random": { "mean_r": 0.40192105943017503, "var_s": 38.97308841420648, "n": 999, "label_GUE": "=0.5996, Var=0.178", "label_Poi": "=0.3863, Var=1.0" }, "inverse_curv_step2_zeta": { "mean_r": 0.6167428093261065, "var_s": 154.45470517440015, "n": 999, "label_GUE": "=0.5996, Var=0.178", "label_Poi": "=0.3863, Var=1.0" }, "inverse_curv_step2_random": { "mean_r": 0.401921246560102, "var_s": 38.957140322605355, "n": 999, "label_GUE": "=0.5996, Var=0.178", "label_Poi": "=0.3863, Var=1.0" }, "inverse_curv_step3_zeta": { "mean_r": 0.6167370753643339, "var_s": 156.5269765327383, "n": 999, "label_GUE": "=0.5996, Var=0.178", "label_Poi": "=0.3863, Var=1.0" }, "inverse_curv_step3_random": { "mean_r": 0.4019210482818952, "var_s": 39.25501258791564, "n": 999, "label_GUE": "=0.5996, Var=0.178", "label_Poi": "=0.3863, Var=1.0" }, "ratio_inv_fwd_zeta": { "mean_r": 0.616702827491862, "var_s": 169.75006856193076, "n": 999, "label_GUE": "=0.5996, Var=0.178", "label_Poi": "=0.3863, Var=1.0" }, "ratio_inv_fwd_random": { "mean_r": 0.4019206104657337, "var_s": 41.511780678856574, "n": 999, "label_GUE": "=0.5996, Var=0.178", "label_Poi": "=0.3863, Var=1.0" } } exec /bin/bash -lc "sed -n '1,220p' tools/data/reports/exp_geodesic_deviation_primes.json 2>/dev/null || true" in /opt/MM_D-ND succeeded in 0ms: { "experiment": "exp_geodesic_deviation_primes", "timestamp": "2026-04-05T12:07:44.263663", "piano": 39, "tension": "METRIC_TENSOR", "claim_tested": "g=(p/2)^2 de Sitter \u2014 is R=2 specific to primes?", "answer": "NO. R=2 is tautological. The FLUCTUATIONS dR=R-2 encode prime structure.", "N_primes": 664579, "prime_dR_std": 0.00476875, "prime_acf_lag1": -0.417203, "prime_acf_lag2": 0.045462, "shuffled_baseline": { "n_surrogates": 20, "dR_std_mean": 0.00633108, "z_score_std": -12.71, "z_score_acf1": -2.16, "z_score_acf2": 0.62 }, "cramer_baseline": { "n_surrogates": 20, "dR_std_mean": 0.00950148, "z_score_std": -46.49, "z_score_acf1": -8.07 }, "psd_ratios_by_band": [ 0.0165, 0.0572, 0.0365, 0.0911, 0.2991 ], "verdict": "CONSTRAINT", "finding": "R=2 tautological. Curvature fluctuations encode gap correlations." } exec /bin/bash -lc "sed -n '1,220p' tools/data/reports/exp_metric_tensor_diag_long.json 2>/dev/null || true" in /opt/MM_D-ND succeeded in 0ms: { "experiment": "exp_metric_tensor_diagnostic_long", "timestamp": "2026-04-06T07:02:53.958098", "piano": 39, "tension": "METRIC_TENSOR", "intensity": 0.9, "claim_tested": "g=(p/2)^2 de Sitter: WHERE does prime structure live? Curvature R vs connection DeltaGamma", "N_primes": 664579, "prime_range": "2 to 9999991", "christoffel": { "Gamma_mean": 0.9999994556821001, "Gamma_std": 0.00015238458344309772 }, "delta_gamma": { "DG_mean": 7.737515044449073e-08, "DG_std": 0.00010494151626705348, "DG_ratio_mean": -0.6335328829054736, "DG_ratio_median": -0.637634229541219 }, "curvature_fluctuations": { "dR_mean": -4.76886861233805, "dR_std": 2125966.232823183 }, "gap_ratio_r": 0.4537691241430244, "z_scores": { "DG_std_vs_cramer": -1.02, "DG_std_vs_shuffled": -2.64, "DG_ratio_median_vs_cramer": -1.52, "DG_ratio_median_vs_shuffled": -0.54, "gap_r_vs_cramer": 71.47, "gap_r_vs_shuffled": -69.95, "dR_std_vs_cramer": -70.49, "dR_std_vs_shuffled": -1.73 }, "cross_window_correlation": { "DG_ratio_vs_gap_r": 0.779 }, "spectral_DG_over_dR_by_band": [ 0.0, 0.0, 0.0, 0.0, 0.0 ], "phi_search_DG_ratios": { "density_at_phi": 0.2137, "density_at_inv_phi": 0.5577, "density_at_1": 0.6761, "density_at_0.5": 0.5611 }, "scaling": { "DG_std_slope_lnp": -1.5535, "dR_std_slope_lnp": 0.9543, "note": "DG_std ~ exp(slope*ln(p)) = p^slope" }, "windows": [ { "p_center": 104736.0, "ln_p": 11.559198177300907, "DG_std": 0.0006049547641905017, "DG_ratio_median": -0.550556344690336, "gap_r_mean": 0.46865368987626965, "dR_std": 50935.45168457889, "dR_acf1": -0.5270276504359691 }, { "p_center": 531284.0, "ln_p": 13.183051997172852, "DG_std": 9.648116925442297e-10, "DG_ratio_median": -0.5345066767019397, "gap_r_mean": 0.4608536285153977, "dR_std": 213220.29224229185, "dR_acf1": -0.5319193688839129 }, { "p_center": 991029.0, "ln_p": 13.806499076254294, "DG_std": 3.6508579206258855e-10, "DG_ratio_median": -0.5840849201386166, "gap_r_mean": 0.4544293247456739, "dR_std": 393695.4934517912, "dR_acf1": -0.5358452536722458 }, { "p_center": 1466046.0, "ln_p": 14.198079538835502, "DG_std": 3.633848627916048e-10, "DG_ratio_median": -0.6172061968318664, "gap_r_mean": 0.45525388702743136, "dR_std": 573921.740299336, "dR_acf1": -0.5294417426053162 }, { "p_center": 1952936.0, "ln_p": 14.48484443922458, "DG_std": 4.377438520666273e-10, "DG_ratio_median": -0.6327923747534422, "gap_r_mean": 0.45407641253660164, "dR_std": 762464.1724560896, "dR_acf1": -0.5312590355011374 }, { "p_center": 2449225.0, "ln_p": 14.711282205948507, "DG_std": 5.264644350100965e-10, "DG_ratio_median": -0.6403577027561954, "gap_r_mean": 0.4559498292222872, "dR_std": 932150.7396741211, "dR_acf1": -0.527865927525674 }, { "p_center": 2951436.0, "ln_p": 14.897802389538235, "DG_std": 6.353051878484298e-10, "DG_ratio_median": -0.6482981444501784, "gap_r_mean": 0.45700764166267005, "dR_std": 1129105.66981058, "dR_acf1": -0.5219803856714409 }, { "p_center": 3458649.0, "ln_p": 15.056388608355858, "DG_std": 7.445838356687753e-10, "DG_ratio_median": -0.6438333753343143, "gap_r_mean": 0.4511002300744175, "dR_std": 1327814.0262547315, "dR_acf1": -0.5241296380930679 }, { "p_center": 3973029.0, "ln_p": 15.19503933404017, "DG_std": 8.631627021849287e-10, "DG_ratio_median": -0.6597487870472384, "gap_r_mean": 0.45222278856392545, "dR_std": 1525884.9570536292, "dR_acf1": -0.5349790585912445 }, { "p_center": 4490240.0, "ln_p": 15.317416710405736, "DG_std": 9.409926622494196e-10, "DG_ratio_median": -0.6540355335442812, "gap_r_mean": 0.4539204758556866, "dR_std": 1692634.5753720046, "dR_acf1": -0.5253635075646966 }, { "p_center": 5011552.0, "ln_p": 15.427256205528115, "DG_std": 1.0436862201908947e-09, "DG_ratio_median": -0.6516839988576755, "gap_r_mean": 0.45623343364203006, "dR_std": 1872661.0947325325, "dR_acf1": -0.5282603903199101 }, { "p_center": 5537154.0, "ln_p": 15.526991208313403, "DG_std": 1.1619497269947163e-09, "DG_ratio_median": -0.6456207341196168, "gap_r_mean": 0.45015138568007956, "dR_std": 2108681.569284433, "dR_acf1": -0.5311617234983693 }, { "p_center": 6066522.0, "ln_p": 15.618296016940745, "DG_std": 1.2939371303383878e-09, "DG_ratio_median": -0.6478527521056698, "gap_r_mean": 0.45116861594031377, "dR_std": 2315672.7692984086, "dR_acf1": -0.5272342095632299 }, { "p_center": 6598020.0, "ln_p": 15.702280161987652, "DG_std": 1.359397046916897e-09, "DG_ratio_median": -0.6556800268910704, "gap_r_mean": 0.4512498393841245, "dR_std": 2477294.5309505574, "dR_acf1": -0.523817431593783 }, { "p_center": 7130862.0, "ln_p": 15.779942682698659, "DG_std": 1.4862453738818192e-09, "DG_ratio_median": -0.6579495317987364, "gap_r_mean": 0.4519834248138264, "dR_std": 2665537.0478511187, "dR_acf1": -0.5279388285290465 }, { "p_center": 7668003.0, "ln_p": 15.852566774383902, "DG_std": 1.6326498507667926e-09, "DG_ratio_median": -0.6593439827209685, "gap_r_mean": 0.44797452671137405, "dR_std": 2929620.38829857, "dR_acf1": -0.5263876663326793 }, { "p_center": 8207490.0, "ln_p": 15.920557709959336, "DG_std": 1.7227792165351079e-09, "DG_ratio_median": -0.6577292531913267, "gap_r_mean": 0.4507419405003188, "dR_std": 3076296.092479103, "dR_acf1": -0.5302365500156548 }, { "p_center": 8747949.0, "ln_p": 15.984329830857824, "DG_std": 2.015584937789757e-09, "DG_ratio_median": -0.6652842443755561, "gap_r_mean": 0.4505903833755405, "dR_std": 3271931.5683313403, "dR_acf1": -0.5210272116954996 }, { "p_center": 9291720.0, exec /bin/bash -lc 'ls -R tools/data/odlyzko_cache 2>/dev/null | head -20' in /opt/MM_D-ND succeeded in 0ms: tools/data/odlyzko_cache: zeros1.txt zeros2.txt exec /bin/bash -lc 'rg -n "def compute_dR|DeltaGamma|residual_at_step|load_zeta_zeros|def run" tools/exp_metric_tensor_diagnostic.py tools/dnd_trace_bridge.py' in /opt/MM_D-ND succeeded in 0ms: tools/dnd_trace_bridge.py:92:def load_zeta_zeros(n_zeros=2000): tools/dnd_trace_bridge.py:151:def run(): tools/dnd_trace_bridge.py:157: zeros = load_zeta_zeros(2000) tools/dnd_trace_bridge.py:209: def residual_at_step(points, step=5): tools/dnd_trace_bridge.py:219: R_zeta = residual_at_step(zeros, step) tools/dnd_trace_bridge.py:220: R_rand = residual_at_step(random_pts, step) tools/exp_metric_tensor_diagnostic.py:8:- Rapporti DeltaGamma => z=+22.5, ma non testato direttamente tools/exp_metric_tensor_diagnostic.py:12:2. Calcola DeltaGamma (variazione gap-to-gap della connessione) tools/exp_metric_tensor_diagnostic.py:13:3. Calcola rapporti DeltaGamma_n/DeltaGamma_{n+1} tools/exp_metric_tensor_diagnostic.py:15:5. Misura il contenuto spettrale di DeltaGamma vs dR tools/exp_metric_tensor_diagnostic.py:59:# ==== 2. DeltaGamma ==== tools/exp_metric_tensor_diagnostic.py:60:DeltaGamma = np.diff(Gamma) tools/exp_metric_tensor_diagnostic.py:61:print(f"DeltaGamma: mean={np.mean(DeltaGamma):.6f}, std={np.std(DeltaGamma):.6f}") tools/exp_metric_tensor_diagnostic.py:63:# ==== 3. Rapporti DeltaGamma consecutivi ==== tools/exp_metric_tensor_diagnostic.py:65:mask = np.abs(DeltaGamma[:-1]) > 1e-20 tools/exp_metric_tensor_diagnostic.py:66:DG_ratios = DeltaGamma[1:][mask] / DeltaGamma[:-1][mask] tools/exp_metric_tensor_diagnostic.py:69:print(f"DeltaGamma ratios: mean={np.mean(DG_ratios_clipped):.6f}, median={np.median(DG_ratios_clipped):.6f}") tools/exp_metric_tensor_diagnostic.py:164:z_DG_std_cramer = zscore(np.std(DeltaGamma), results_surr['cramer']['DG_std']) tools/exp_metric_tensor_diagnostic.py:165:z_DG_std_shuffled = zscore(np.std(DeltaGamma), results_surr['shuffled']['DG_std']) tools/exp_metric_tensor_diagnostic.py:178:print(f"DeltaGamma std: z_cramer={z_DG_std_cramer:.2f}, z_shuffled={z_DG_std_shuffled:.2f}") tools/exp_metric_tensor_diagnostic.py:179:print(f"DeltaGamma ratio median: z_cramer={z_DGratio_cramer:.2f}, z_shuffled={z_DGratio_shuffled:.2f}") tools/exp_metric_tensor_diagnostic.py:196: # DeltaGamma in window tools/exp_metric_tensor_diagnostic.py:242:# ==== 10. Spectral comparison: DeltaGamma vs dR ==== tools/exp_metric_tensor_diagnostic.py:248:DG_chunk = DeltaGamma[start:start+chunk] tools/exp_metric_tensor_diagnostic.py:324: "claim_tested": "g=(p/2)^2 de Sitter: WHERE does prime structure live? Curvature R vs connection DeltaGamma", tools/exp_metric_tensor_diagnostic.py:333: "DG_mean": float(np.mean(DeltaGamma)), tools/exp_metric_tensor_diagnostic.py:334: "DG_std": float(np.std(DeltaGamma)), tools/exp_metric_tensor_diagnostic.py:394:print(f"DeltaGamma std: {output['delta_gamma']['DG_std']:.6e}") exec /bin/bash -lc "find . -maxdepth 3 -type f | rg 'bridge|metric|zeta|prime|entropy|thermo|quantum|gravity' | head -80" in /opt/MM_D-ND succeeded in 0ms: ./tools/zeta_validation.py ./tools/__pycache__/semantic_bridge.cpython-312.pyc ./tools/__pycache__/zeta_validation.cpython-312.pyc ./tools/r_stat_primes.py ./tools/exp_geodesic_deviation_primes.py ./tools/dnd_trace_bridge.py ./tools/exp_psd_prime_gaps.py ./tools/gap_ratio_primes.py ./tools/exp_ricci_primes.py ./tools/dnd_spettro_zeta.py ./tools/dnd_trace_bridge_v3.py ./tools/semantic_bridge.py ./tools/dnd_zeros_vs_zeta.py ./tools/exp_metric_tensor_diagnostic.py ./tools/data/prime_gaps_spectrum.png ./tools/data/trace_bridge_results.json ./tools/data/psd_prime_gaps_results.json ./tools/data/prime_gaps_spectrum_pub.png ./tools/data/spettro_zeta_results.json ./tools/data/zeta_validation_results.json ./tools/data/lab_bridge_issues.jsonl ./figures/dnd_zeros_vs_zeta.png ./figures/dnd_zeros_vs_zeta.svg ./figures/dnd_zeros_vs_zeta.pdf ./awareness/1_Φ_INFERENTIAL/2025-12-08_13-44-36__a-thermo-collapse-d-nd-euf-ising-dynamics.html ./awareness/1_Φ_INFERENTIAL/2025-12-15_18-49-56__simulazione-numerica-fp-gce-con-pythonjax-e-metriche-_ma1.md ./papers/notes/P5_autopoiesis_bridge_F.md codex Creo uno strumento riusabile invece di un report manuale: stesso gate del cycle 09:23, ma con generatori di perimetro “bridge” diversi. I perimetri saranno metrici/trace/QxE, così il test può anche produrre un blank o un contro-scope senza cambiare soglia. apply patch patch: completed /opt/MM_D-ND/tools/exp_bridge_order_denominator_gate.py diff --git a/tools/exp_bridge_order_denominator_gate.py b/tools/exp_bridge_order_denominator_gate.py new file mode 100644 index 0000000000000000000000000000000000000000..17286d6199454f089722ec1e0e6bad01f25f3b92 --- /dev/null +++ b/tools/exp_bridge_order_denominator_gate.py @@ -0,0 +1,206 @@ +#!/usr/bin/env python3 +""" +exp_bridge_order_denominator_gate.py + +Falsification attempt for ORDER_DENOMINATOR_GATE on bridge/perimeter sequences +already present in the D-ND lab context: + +- prime metric connection fluctuations from g=(p/2)^2 +- prime metric curvature fluctuations dR +- zeta trace-bridge nonlinear residuals +- hydrogen bound-level spacings from the QxE bridge + +The coherent endpoint is the observed/generated bridge order. The illusory +endpoint is a marginal-preserving permutation. Canonical observables come from +observables_registry.py. +""" + +from __future__ import annotations + +import argparse +import json +import math +from pathlib import Path + +import numpy as np + +from exp_semireal_order_denominator_gate import analyze_sequence, compact, normalize +from observables_registry import OBSERVABLES_REGISTRY_VERSION, OBSERVABLES_CANONICAL + + +OBS_NAMES = list(OBSERVABLES_CANONICAL.keys()) +PHI = (1.0 + math.sqrt(5.0)) / 2.0 +LAMBDA = -1.0 / PHI**2 +DATA_DIR = Path(__file__).parent / "data" + + +def sieve_primes_for_count(n_primes: int) -> np.ndarray: + if n_primes < 6: + limit = 20 + else: + limit = int(n_primes * (math.log(n_primes) + math.log(math.log(n_primes))) * 1.35) + while True: + sieve = np.ones(limit + 1, dtype=bool) + sieve[:2] = False + for p in range(2, int(limit**0.5) + 1): + if sieve[p]: + sieve[p * p : limit + 1 : p] = False + primes = np.flatnonzero(sieve) + if len(primes) >= n_primes: + return primes[:n_primes].astype(float) + limit *= 2 + + +def positive_bridge_values(values: np.ndarray) -> np.ndarray: + """Map a signed bridge observable to positive values without changing order.""" + values = np.asarray(values, dtype=float) + values = values[np.isfinite(values)] + values = np.abs(values) + return normalize(values + 1e-12) + + +def prime_metric_delta_gamma(n_values: int) -> np.ndarray: + primes = sieve_primes_for_count(n_values + 3) + p = primes.astype(float) + tau = np.log(p) + metric = (p / 2.0) ** 2 + dg = np.diff(metric) + dtau = np.diff(tau) + mid = (metric[:-1] + metric[1:]) / 2.0 + gamma = dg / (2.0 * mid * dtau) + delta_gamma = np.diff(gamma) + return positive_bridge_values(delta_gamma[:n_values]) + + +def prime_metric_dR(n_values: int) -> np.ndarray: + primes = sieve_primes_for_count(n_values + 3) + seq = primes.astype(float) + t = np.log(seq) + a = seq / 2.0 + dt = np.diff(t) + dt_mid = (dt[:-1] + dt[1:]) / 2.0 + da = np.diff(a) + a_prime = da / dt + da_prime = np.diff(a_prime) + a_double_prime = da_prime / dt_mid + r_n = 2.0 * a_double_prime / a[1:-1] + d_r = r_n - 2.0 + return positive_bridge_values(d_r[:n_values]) + + +def load_zeta_zeros(n_zeros: int) -> np.ndarray: + zeros_file = DATA_DIR / "odlyzko_cache" / "zeros1.txt" + if not zeros_file.exists(): + raise RuntimeError(f"{zeros_file} not found") + zeros: list[float] = [] + with zeros_file.open() as f: + for line in f: + line = line.strip() + if not line: + continue + zeros.append(float(line)) + if len(zeros) >= n_zeros: + break + if len(zeros) < n_zeros: + raise RuntimeError(f"only {len(zeros)} zeta zeros available, need {n_zeros}") + return np.array(zeros, dtype=float) + + +def dnd_map_trajectory(x0: float, n_iter: int) -> np.ndarray: + x = float(x0) + traj = [x] + for _ in range(n_iter): + if abs(x) < 1e-15: + break + x = 1.0 + 1.0 / x + if not np.isfinite(x): + break + traj.append(x) + return np.array(traj, dtype=float) + + +def zeta_trace_residual(n_values: int, step: int = 5) -> np.ndarray: + zeros = load_zeta_zeros(n_values) + residuals = [] + for x0 in zeros: + traj = dnd_map_trajectory(float(x0), max(step + 2, 15)) + if len(traj) <= step: + continue + linear = PHI + (float(x0) - PHI) * (LAMBDA**step) + residuals.append(traj[step] - linear) + return positive_bridge_values(np.array(residuals[:n_values], dtype=float)) + + +def hydrogen_bound_level_spacings(n_values: int) -> np.ndarray: + # Atomic units: E_n = -1/(2n^2). Positive adjacent spacings shrink smoothly. + n = np.arange(1, n_values + 2, dtype=float) + energy = -1.0 / (2.0 * n**2) + spacings = np.diff(energy) + return normalize(spacings) + + +def build_sequences(args: argparse.Namespace) -> dict[str, np.ndarray]: + return { + "prime_metric_delta_gamma_abs": prime_metric_delta_gamma(args.n_gaps), + "prime_metric_dR_abs": prime_metric_dR(args.n_gaps), + "zeta_trace_residual_step5_abs": zeta_trace_residual(args.zeta_values, step=5), + "hydrogen_bound_level_spacings": hydrogen_bound_level_spacings(args.n_gaps), + } + + +def run(args: argparse.Namespace) -> dict: + root_rng = np.random.default_rng(args.seed) + sequences = build_sequences(args) + perimeters = {} + for name, base in sequences.items(): + perimeters[name] = analyze_sequence(name, base, args, root_rng) + + output = { + "experiment": "bridge_order_denominator_gate", + "category": "gate_falsification_bridge", + "question": "Does ORDER_DENOMINATOR_GATE survive on D-ND bridge sequences beyond prime/zeta/logistic gaps?", + "observables_registry": OBSERVABLES_REGISTRY_VERSION, + "observables_used": OBS_NAMES, + "params": vars(args), + "matrix": compact(perimeters), + "perimeters": perimeters, + } + + out_path = Path(args.out) + out_path.parent.mkdir(parents=True, exist_ok=True) + with out_path.open("w") as f: + json.dump(output, f, indent=2) + + print(f"observables_registry={OBSERVABLES_REGISTRY_VERSION}") + print(f"observables_used={OBS_NAMES}") + print("perimeter n one_sided stable0 stable1 dist_gate ambiguous_gate") + for name, row in output["matrix"].items(): + print( + f"{name:>34s} " + f"{row['n_gaps']:>5d} " + f"{','.join(row['coherent_one_sided_observables']) or '[]':>22s} " + f"{row['stable_count_coherent']:>7.3f} " + f"{row['stable_count_illusory']:>7.3f} " + f"{row['endpoint_distance_one_sided_gated']:>9.3f} " + f"{row['ambiguous_beta_one_sided_gated']}" + ) + print(f"saved {out_path}") + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--n-gaps", type=int, default=4096) + parser.add_argument("--zeta-values", type=int, default=2000) + parser.add_argument("--n-replicates", type=int, default=20) + parser.add_argument("--n-beta", type=int, default=11) + parser.add_argument("--n-baseline", type=int, default=32) + parser.add_argument("--z-min", type=float, default=2.0) + parser.add_argument("--seed", type=int, default=202605070942) + parser.add_argument("--out", default="tools/data/bridge_order_denominator_gate_20260507_0942.json") + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() diff --git a/tools/exp_bridge_order_denominator_gate.py b/tools/exp_bridge_order_denominator_gate.py new file mode 100644 index 0000000000000000000000000000000000000000..17286d6199454f089722ec1e0e6bad01f25f3b92 --- /dev/null +++ b/tools/exp_bridge_order_denominator_gate.py @@ -0,0 +1,206 @@ +#!/usr/bin/env python3 +""" +exp_bridge_order_denominator_gate.py + +Falsification attempt for ORDER_DENOMINATOR_GATE on bridge/perimeter sequences +already present in the D-ND lab context: + +- prime metric connection fluctuations from g=(p/2)^2 +- prime metric curvature fluctuations dR +- zeta trace-bridge nonlinear residuals +- hydrogen bound-level spacings from the QxE bridge + +The coherent endpoint is the observed/generated bridge order. The illusory +endpoint is a marginal-preserving permutation. Canonical observables come from +observables_registry.py. +""" + +from __future__ import annotations + +import argparse +import json +import math +from pathlib import Path + +import numpy as np + +from exp_semireal_order_denominator_gate import analyze_sequence, compact, normalize +from observables_registry import OBSERVABLES_REGISTRY_VERSION, OBSERVABLES_CANONICAL + + +OBS_NAMES = list(OBSERVABLES_CANONICAL.keys()) +PHI = (1.0 + math.sqrt(5.0)) / 2.0 +LAMBDA = -1.0 / PHI**2 +DATA_DIR = Path(__file__).parent / "data" + + +def sieve_primes_for_count(n_primes: int) -> np.ndarray: + if n_primes < 6: + limit = 20 + else: + limit = int(n_primes * (math.log(n_primes) + math.log(math.log(n_primes))) * 1.35) + while True: + sieve = np.ones(limit + 1, dtype=bool) + sieve[:2] = False + for p in range(2, int(limit**0.5) + 1): + if sieve[p]: + sieve[p * p : limit + 1 : p] = False + primes = np.flatnonzero(sieve) + if len(primes) >= n_primes: + return primes[:n_primes].astype(float) + limit *= 2 + + +def positive_bridge_values(values: np.ndarray) -> np.ndarray: + """Map a signed bridge observable to positive values without changing order.""" + values = np.asarray(values, dtype=float) + values = values[np.isfinite(values)] + values = np.abs(values) + return normalize(values + 1e-12) + + +def prime_metric_delta_gamma(n_values: int) -> np.ndarray: + primes = sieve_primes_for_count(n_values + 3) + p = primes.astype(float) + tau = np.log(p) + metric = (p / 2.0) ** 2 + dg = np.diff(metric) + dtau = np.diff(tau) + mid = (metric[:-1] + metric[1:]) / 2.0 + gamma = dg / (2.0 * mid * dtau) + delta_gamma = np.diff(gamma) + return positive_bridge_values(delta_gamma[:n_values]) + + +def prime_metric_dR(n_values: int) -> np.ndarray: + primes = sieve_primes_for_count(n_values + 3) + seq = primes.astype(float) + t = np.log(seq) + a = seq / 2.0 + dt = np.diff(t) + dt_mid = (dt[:-1] + dt[1:]) / 2.0 + da = np.diff(a) + a_prime = da / dt + da_prime = np.diff(a_prime) + a_double_prime = da_prime / dt_mid + r_n = 2.0 * a_double_prime / a[1:-1] + d_r = r_n - 2.0 + return positive_bridge_values(d_r[:n_values]) + + +def load_zeta_zeros(n_zeros: int) -> np.ndarray: + zeros_file = DATA_DIR / "odlyzko_cache" / "zeros1.txt" + if not zeros_file.exists(): + raise RuntimeError(f"{zeros_file} not found") + zeros: list[float] = [] + with zeros_file.open() as f: + for line in f: + line = line.strip() + if not line: + continue + zeros.append(float(line)) + if len(zeros) >= n_zeros: + break + if len(zeros) < n_zeros: + raise RuntimeError(f"only {len(zeros)} zeta zeros available, need {n_zeros}") + return np.array(zeros, dtype=float) + + +def dnd_map_trajectory(x0: float, n_iter: int) -> np.ndarray: + x = float(x0) + traj = [x] + for _ in range(n_iter): + if abs(x) < 1e-15: + break + x = 1.0 + 1.0 / x + if not np.isfinite(x): + break + traj.append(x) + return np.array(traj, dtype=float) + + +def zeta_trace_residual(n_values: int, step: int = 5) -> np.ndarray: + zeros = load_zeta_zeros(n_values) + residuals = [] + for x0 in zeros: + traj = dnd_map_trajectory(float(x0), max(step + 2, 15)) + if len(traj) <= step: + continue + linear = PHI + (float(x0) - PHI) * (LAMBDA**step) + residuals.append(traj[step] - linear) + return positive_bridge_values(np.array(residuals[:n_values], dtype=float)) + + +def hydrogen_bound_level_spacings(n_values: int) -> np.ndarray: + # Atomic units: E_n = -1/(2n^2). Positive adjacent spacings shrink smoothly. + n = np.arange(1, n_values + 2, dtype=float) + energy = -1.0 / (2.0 * n**2) + spacings = np.diff(energy) + return normalize(spacings) + + +def build_sequences(args: argparse.Namespace) -> dict[str, np.ndarray]: + return { + "prime_metric_delta_gamma_abs": prime_metric_delta_gamma(args.n_gaps), + "prime_metric_dR_abs": prime_metric_dR(args.n_gaps), + "zeta_trace_residual_step5_abs": zeta_trace_residual(args.zeta_values, step=5), + "hydrogen_bound_level_spacings": hydrogen_bound_level_spacings(args.n_gaps), + } + + +def run(args: argparse.Namespace) -> dict: + root_rng = np.random.default_rng(args.seed) + sequences = build_sequences(args) + perimeters = {} + for name, base in sequences.items(): + perimeters[name] = analyze_sequence(name, base, args, root_rng) + + output = { + "experiment": "bridge_order_denominator_gate", + "category": "gate_falsification_bridge", + "question": "Does ORDER_DENOMINATOR_GATE survive on D-ND bridge sequences beyond prime/zeta/logistic gaps?", + "observables_registry": OBSERVABLES_REGISTRY_VERSION, + "observables_used": OBS_NAMES, + "params": vars(args), + "matrix": compact(perimeters), + "perimeters": perimeters, + } + + out_path = Path(args.out) + out_path.parent.mkdir(parents=True, exist_ok=True) + with out_path.open("w") as f: + json.dump(output, f, indent=2) + + print(f"observables_registry={OBSERVABLES_REGISTRY_VERSION}") + print(f"observables_used={OBS_NAMES}") + print("perimeter n one_sided stable0 stable1 dist_gate ambiguous_gate") + for name, row in output["matrix"].items(): + print( + f"{name:>34s} " + f"{row['n_gaps']:>5d} " + f"{','.join(row['coherent_one_sided_observables']) or '[]':>22s} " + f"{row['stable_count_coherent']:>7.3f} " + f"{row['stable_count_illusory']:>7.3f} " + f"{row['endpoint_distance_one_sided_gated']:>9.3f} " + f"{row['ambiguous_beta_one_sided_gated']}" + ) + print(f"saved {out_path}") + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--n-gaps", type=int, default=4096) + parser.add_argument("--zeta-values", type=int, default=2000) + parser.add_argument("--n-replicates", type=int, default=20) + parser.add_argument("--n-beta", type=int, default=11) + parser.add_argument("--n-baseline", type=int, default=32) + parser.add_argument("--z-min", type=float, default=2.0) + parser.add_argument("--seed", type=int, default=202605070942) + parser.add_argument("--out", default="tools/data/bridge_order_denominator_gate_20260507_0942.json") + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() codex Lo strumento è pronto. Lo eseguo con i parametri standard del gate e poi faccio un seed-check più leggero per vedere se il pattern regge o se cade per rumore di campionamento. exec /bin/bash -lc 'python tools/exp_bridge_order_denominator_gate.py --n-replicates 20 --n-beta 11 --n-baseline 32 --seed 202605070942 --out tools/data/bridge_order_denominator_gate_20260507_0942.json' in /opt/MM_D-ND diff --git a/tools/exp_bridge_order_denominator_gate.py b/tools/exp_bridge_order_denominator_gate.py new file mode 100644 index 0000000000000000000000000000000000000000..17286d6199454f089722ec1e0e6bad01f25f3b92 --- /dev/null +++ b/tools/exp_bridge_order_denominator_gate.py @@ -0,0 +1,206 @@ +#!/usr/bin/env python3 +""" +exp_bridge_order_denominator_gate.py + +Falsification attempt for ORDER_DENOMINATOR_GATE on bridge/perimeter sequences +already present in the D-ND lab context: + +- prime metric connection fluctuations from g=(p/2)^2 +- prime metric curvature fluctuations dR +- zeta trace-bridge nonlinear residuals +- hydrogen bound-level spacings from the QxE bridge + +The coherent endpoint is the observed/generated bridge order. The illusory +endpoint is a marginal-preserving permutation. Canonical observables come from +observables_registry.py. +""" + +from __future__ import annotations + +import argparse +import json +import math +from pathlib import Path + +import numpy as np + +from exp_semireal_order_denominator_gate import analyze_sequence, compact, normalize +from observables_registry import OBSERVABLES_REGISTRY_VERSION, OBSERVABLES_CANONICAL + + +OBS_NAMES = list(OBSERVABLES_CANONICAL.keys()) +PHI = (1.0 + math.sqrt(5.0)) / 2.0 +LAMBDA = -1.0 / PHI**2 +DATA_DIR = Path(__file__).parent / "data" + + +def sieve_primes_for_count(n_primes: int) -> np.ndarray: + if n_primes < 6: + limit = 20 + else: + limit = int(n_primes * (math.log(n_primes) + math.log(math.log(n_primes))) * 1.35) + while True: + sieve = np.ones(limit + 1, dtype=bool) + sieve[:2] = False + for p in range(2, int(limit**0.5) + 1): + if sieve[p]: + sieve[p * p : limit + 1 : p] = False + primes = np.flatnonzero(sieve) + if len(primes) >= n_primes: + return primes[:n_primes].astype(float) + limit *= 2 + + +def positive_bridge_values(values: np.ndarray) -> np.ndarray: + """Map a signed bridge observable to positive values without changing order.""" + values = np.asarray(values, dtype=float) + values = values[np.isfinite(values)] + values = np.abs(values) + return normalize(values + 1e-12) + + +def prime_metric_delta_gamma(n_values: int) -> np.ndarray: + primes = sieve_primes_for_count(n_values + 3) + p = primes.astype(float) + tau = np.log(p) + metric = (p / 2.0) ** 2 + dg = np.diff(metric) + dtau = np.diff(tau) + mid = (metric[:-1] + metric[1:]) / 2.0 + gamma = dg / (2.0 * mid * dtau) + delta_gamma = np.diff(gamma) + return positive_bridge_values(delta_gamma[:n_values]) + + +def prime_metric_dR(n_values: int) -> np.ndarray: + primes = sieve_primes_for_count(n_values + 3) + seq = primes.astype(float) + t = np.log(seq) + a = seq / 2.0 + dt = np.diff(t) + dt_mid = (dt[:-1] + dt[1:]) / 2.0 + da = np.diff(a) + a_prime = da / dt + da_prime = np.diff(a_prime) + a_double_prime = da_prime / dt_mid + r_n = 2.0 * a_double_prime / a[1:-1] + d_r = r_n - 2.0 + return positive_bridge_values(d_r[:n_values]) + + +def load_zeta_zeros(n_zeros: int) -> np.ndarray: + zeros_file = DATA_DIR / "odlyzko_cache" / "zeros1.txt" + if not zeros_file.exists(): + raise RuntimeError(f"{zeros_file} not found") + zeros: list[float] = [] + with zeros_file.open() as f: + for line in f: + line = line.strip() + if not line: + continue + zeros.append(float(line)) + if len(zeros) >= n_zeros: + break + if len(zeros) < n_zeros: + raise RuntimeError(f"only {len(zeros)} zeta zeros available, need {n_zeros}") + return np.array(zeros, dtype=float) + + +def dnd_map_trajectory(x0: float, n_iter: int) -> np.ndarray: + x = float(x0) + traj = [x] + for _ in range(n_iter): + if abs(x) < 1e-15: + break + x = 1.0 + 1.0 / x + if not np.isfinite(x): + break + traj.append(x) + return np.array(traj, dtype=float) + + +def zeta_trace_residual(n_values: int, step: int = 5) -> np.ndarray: + zeros = load_zeta_zeros(n_values) + residuals = [] + for x0 in zeros: + traj = dnd_map_trajectory(float(x0), max(step + 2, 15)) + if len(traj) <= step: + continue + linear = PHI + (float(x0) - PHI) * (LAMBDA**step) + residuals.append(traj[step] - linear) + return positive_bridge_values(np.array(residuals[:n_values], dtype=float)) + + +def hydrogen_bound_level_spacings(n_values: int) -> np.ndarray: + # Atomic units: E_n = -1/(2n^2). Positive adjacent spacings shrink smoothly. + n = np.arange(1, n_values + 2, dtype=float) + energy = -1.0 / (2.0 * n**2) + spacings = np.diff(energy) + return normalize(spacings) + + +def build_sequences(args: argparse.Namespace) -> dict[str, np.ndarray]: + return { + "prime_metric_delta_gamma_abs": prime_metric_delta_gamma(args.n_gaps), + "prime_metric_dR_abs": prime_metric_dR(args.n_gaps), + "zeta_trace_residual_step5_abs": zeta_trace_residual(args.zeta_values, step=5), + "hydrogen_bound_level_spacings": hydrogen_bound_level_spacings(args.n_gaps), + } + + +def run(args: argparse.Namespace) -> dict: + root_rng = np.random.default_rng(args.seed) + sequences = build_sequences(args) + perimeters = {} + for name, base in sequences.items(): + perimeters[name] = analyze_sequence(name, base, args, root_rng) + + output = { + "experiment": "bridge_order_denominator_gate", + "category": "gate_falsification_bridge", + "question": "Does ORDER_DENOMINATOR_GATE survive on D-ND bridge sequences beyond prime/zeta/logistic gaps?", + "observables_registry": OBSERVABLES_REGISTRY_VERSION, + "observables_used": OBS_NAMES, + "params": vars(args), + "matrix": compact(perimeters), + "perimeters": perimeters, + } + + out_path = Path(args.out) + out_path.parent.mkdir(parents=True, exist_ok=True) + with out_path.open("w") as f: + json.dump(output, f, indent=2) + + print(f"observables_registry={OBSERVABLES_REGISTRY_VERSION}") + print(f"observables_used={OBS_NAMES}") + print("perimeter n one_sided stable0 stable1 dist_gate ambiguous_gate") + for name, row in output["matrix"].items(): + print( + f"{name:>34s} " + f"{row['n_gaps']:>5d} " + f"{','.join(row['coherent_one_sided_observables']) or '[]':>22s} " + f"{row['stable_count_coherent']:>7.3f} " + f"{row['stable_count_illusory']:>7.3f} " + f"{row['endpoint_distance_one_sided_gated']:>9.3f} " + f"{row['ambiguous_beta_one_sided_gated']}" + ) + print(f"saved {out_path}") + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--n-gaps", type=int, default=4096) + parser.add_argument("--zeta-values", type=int, default=2000) + parser.add_argument("--n-replicates", type=int, default=20) + parser.add_argument("--n-beta", type=int, default=11) + parser.add_argument("--n-baseline", type=int, default=32) + parser.add_argument("--z-min", type=float, default=2.0) + parser.add_argument("--seed", type=int, default=202605070942) + parser.add_argument("--out", default="tools/data/bridge_order_denominator_gate_20260507_0942.json") + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() diff --git a/tools/exp_bridge_order_denominator_gate.py b/tools/exp_bridge_order_denominator_gate.py new file mode 100644 index 0000000000000000000000000000000000000000..17286d6199454f089722ec1e0e6bad01f25f3b92 --- /dev/null +++ b/tools/exp_bridge_order_denominator_gate.py @@ -0,0 +1,206 @@ +#!/usr/bin/env python3 +""" +exp_bridge_order_denominator_gate.py + +Falsification attempt for ORDER_DENOMINATOR_GATE on bridge/perimeter sequences +already present in the D-ND lab context: + +- prime metric connection fluctuations from g=(p/2)^2 +- prime metric curvature fluctuations dR +- zeta trace-bridge nonlinear residuals +- hydrogen bound-level spacings from the QxE bridge + +The coherent endpoint is the observed/generated bridge order. The illusory +endpoint is a marginal-preserving permutation. Canonical observables come from +observables_registry.py. +""" + +from __future__ import annotations + +import argparse +import json +import math +from pathlib import Path + +import numpy as np + +from exp_semireal_order_denominator_gate import analyze_sequence, compact, normalize +from observables_registry import OBSERVABLES_REGISTRY_VERSION, OBSERVABLES_CANONICAL + + +OBS_NAMES = list(OBSERVABLES_CANONICAL.keys()) +PHI = (1.0 + math.sqrt(5.0)) / 2.0 +LAMBDA = -1.0 / PHI**2 +DATA_DIR = Path(__file__).parent / "data" + + +def sieve_primes_for_count(n_primes: int) -> np.ndarray: + if n_primes < 6: + limit = 20 + else: + limit = int(n_primes * (math.log(n_primes) + math.log(math.log(n_primes))) * 1.35) + while True: + sieve = np.ones(limit + 1, dtype=bool) + sieve[:2] = False + for p in range(2, int(limit**0.5) + 1): + if sieve[p]: + sieve[p * p : limit + 1 : p] = False + primes = np.flatnonzero(sieve) + if len(primes) >= n_primes: + return primes[:n_primes].astype(float) + limit *= 2 + + +def positive_bridge_values(values: np.ndarray) -> np.ndarray: + """Map a signed bridge observable to positive values without changing order.""" + values = np.asarray(values, dtype=float) + values = values[np.isfinite(values)] + values = np.abs(values) + return normalize(values + 1e-12) + + +def prime_metric_delta_gamma(n_values: int) -> np.ndarray: + primes = sieve_primes_for_count(n_values + 3) + p = primes.astype(float) + tau = np.log(p) + metric = (p / 2.0) ** 2 + dg = np.diff(metric) + dtau = np.diff(tau) + mid = (metric[:-1] + metric[1:]) / 2.0 + gamma = dg / (2.0 * mid * dtau) + delta_gamma = np.diff(gamma) + return positive_bridge_values(delta_gamma[:n_values]) + + +def prime_metric_dR(n_values: int) -> np.ndarray: + primes = sieve_primes_for_count(n_values + 3) + seq = primes.astype(float) + t = np.log(seq) + a = seq / 2.0 + dt = np.diff(t) + dt_mid = (dt[:-1] + dt[1:]) / 2.0 + da = np.diff(a) + a_prime = da / dt + da_prime = np.diff(a_prime) + a_double_prime = da_prime / dt_mid + r_n = 2.0 * a_double_prime / a[1:-1] + d_r = r_n - 2.0 + return positive_bridge_values(d_r[:n_values]) + + +def load_zeta_zeros(n_zeros: int) -> np.ndarray: + zeros_file = DATA_DIR / "odlyzko_cache" / "zeros1.txt" + if not zeros_file.exists(): + raise RuntimeError(f"{zeros_file} not found") + zeros: list[float] = [] + with zeros_file.open() as f: + for line in f: + line = line.strip() + if not line: + continue + zeros.append(float(line)) + if len(zeros) >= n_zeros: + break + if len(zeros) < n_zeros: + raise RuntimeError(f"only {len(zeros)} zeta zeros available, need {n_zeros}") + return np.array(zeros, dtype=float) + + +def dnd_map_trajectory(x0: float, n_iter: int) -> np.ndarray: + x = float(x0) + traj = [x] + for _ in range(n_iter): + if abs(x) < 1e-15: + break + x = 1.0 + 1.0 / x + if not np.isfinite(x): + break + traj.append(x) + return np.array(traj, dtype=float) + + +def zeta_trace_residual(n_values: int, step: int = 5) -> np.ndarray: + zeros = load_zeta_zeros(n_values) + residuals = [] + for x0 in zeros: + traj = dnd_map_trajectory(float(x0), max(step + 2, 15)) + if len(traj) <= step: + continue + linear = PHI + (float(x0) - PHI) * (LAMBDA**step) + residuals.append(traj[step] - linear) + return positive_bridge_values(np.array(residuals[:n_values], dtype=float)) + + +def hydrogen_bound_level_spacings(n_values: int) -> np.ndarray: + # Atomic units: E_n = -1/(2n^2). Positive adjacent spacings shrink smoothly. + n = np.arange(1, n_values + 2, dtype=float) + energy = -1.0 / (2.0 * n**2) + spacings = np.diff(energy) + return normalize(spacings) + + +def build_sequences(args: argparse.Namespace) -> dict[str, np.ndarray]: + return { + "prime_metric_delta_gamma_abs": prime_metric_delta_gamma(args.n_gaps), + "prime_metric_dR_abs": prime_metric_dR(args.n_gaps), + "zeta_trace_residual_step5_abs": zeta_trace_residual(args.zeta_values, step=5), + "hydrogen_bound_level_spacings": hydrogen_bound_level_spacings(args.n_gaps), + } + + +def run(args: argparse.Namespace) -> dict: + root_rng = np.random.default_rng(args.seed) + sequences = build_sequences(args) + perimeters = {} + for name, base in sequences.items(): + perimeters[name] = analyze_sequence(name, base, args, root_rng) + + output = { + "experiment": "bridge_order_denominator_gate", + "category": "gate_falsification_bridge", + "question": "Does ORDER_DENOMINATOR_GATE survive on D-ND bridge sequences beyond prime/zeta/logistic gaps?", + "observables_registry": OBSERVABLES_REGISTRY_VERSION, + "observables_used": OBS_NAMES, + "params": vars(args), + "matrix": compact(perimeters), + "perimeters": perimeters, + } + + out_path = Path(args.out) + out_path.parent.mkdir(parents=True, exist_ok=True) + with out_path.open("w") as f: + json.dump(output, f, indent=2) + + print(f"observables_registry={OBSERVABLES_REGISTRY_VERSION}") + print(f"observables_used={OBS_NAMES}") + print("perimeter n one_sided stable0 stable1 dist_gate ambiguous_gate") + for name, row in output["matrix"].items(): + print( + f"{name:>34s} " + f"{row['n_gaps']:>5d} " + f"{','.join(row['coherent_one_sided_observables']) or '[]':>22s} " + f"{row['stable_count_coherent']:>7.3f} " + f"{row['stable_count_illusory']:>7.3f} " + f"{row['endpoint_distance_one_sided_gated']:>9.3f} " + f"{row['ambiguous_beta_one_sided_gated']}" + ) + print(f"saved {out_path}") + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--n-gaps", type=int, default=4096) + parser.add_argument("--zeta-values", type=int, default=2000) + parser.add_argument("--n-replicates", type=int, default=20) + parser.add_argument("--n-beta", type=int, default=11) + parser.add_argument("--n-baseline", type=int, default=32) + parser.add_argument("--z-min", type=float, default=2.0) + parser.add_argument("--seed", type=int, default=202605070942) + parser.add_argument("--out", default="tools/data/bridge_order_denominator_gate_20260507_0942.json") + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() succeeded in 3802ms: observables_registry=1.0.0-2026-05-06 observables_used=['SR', 'SR2', 'L1', 'L2', 'triple_var'] perimeter n one_sided stable0 stable1 dist_gate ambiguous_gate prime_metric_delta_gamma_abs 4096 SR,SR2,L1,L2,triple_var 5.000 0.250 4.415 [] prime_metric_dR_abs 4096 SR,SR2,L1,L2,triple_var 5.000 0.350 4.414 [0.30000000000000004] zeta_trace_residual_step5_abs 2000 SR,SR2,L1,L2,triple_var 5.000 0.050 4.415 [0.30000000000000004] hydrogen_bound_level_spacings 4096 SR,SR2,L1,L2,triple_var 5.000 0.200 4.416 [0.4] saved tools/data/bridge_order_denominator_gate_20260507_0942.json exec /bin/bash -lc 'python tools/exp_bridge_order_denominator_gate.py --n-replicates 12 --n-beta 11 --n-baseline 24 --seed 202605070943 --out tools/data/bridge_order_denominator_gate_20260507_0942_seedcheck.json' in /opt/MM_D-ND diff --git a/tools/exp_bridge_order_denominator_gate.py b/tools/exp_bridge_order_denominator_gate.py new file mode 100644 index 0000000000000000000000000000000000000000..17286d6199454f089722ec1e0e6bad01f25f3b92 --- /dev/null +++ b/tools/exp_bridge_order_denominator_gate.py @@ -0,0 +1,206 @@ +#!/usr/bin/env python3 +""" +exp_bridge_order_denominator_gate.py + +Falsification attempt for ORDER_DENOMINATOR_GATE on bridge/perimeter sequences +already present in the D-ND lab context: + +- prime metric connection fluctuations from g=(p/2)^2 +- prime metric curvature fluctuations dR +- zeta trace-bridge nonlinear residuals +- hydrogen bound-level spacings from the QxE bridge + +The coherent endpoint is the observed/generated bridge order. The illusory +endpoint is a marginal-preserving permutation. Canonical observables come from +observables_registry.py. +""" + +from __future__ import annotations + +import argparse +import json +import math +from pathlib import Path + +import numpy as np + +from exp_semireal_order_denominator_gate import analyze_sequence, compact, normalize +from observables_registry import OBSERVABLES_REGISTRY_VERSION, OBSERVABLES_CANONICAL + + +OBS_NAMES = list(OBSERVABLES_CANONICAL.keys()) +PHI = (1.0 + math.sqrt(5.0)) / 2.0 +LAMBDA = -1.0 / PHI**2 +DATA_DIR = Path(__file__).parent / "data" + + +def sieve_primes_for_count(n_primes: int) -> np.ndarray: + if n_primes < 6: + limit = 20 + else: + limit = int(n_primes * (math.log(n_primes) + math.log(math.log(n_primes))) * 1.35) + while True: + sieve = np.ones(limit + 1, dtype=bool) + sieve[:2] = False + for p in range(2, int(limit**0.5) + 1): + if sieve[p]: + sieve[p * p : limit + 1 : p] = False + primes = np.flatnonzero(sieve) + if len(primes) >= n_primes: + return primes[:n_primes].astype(float) + limit *= 2 + + +def positive_bridge_values(values: np.ndarray) -> np.ndarray: + """Map a signed bridge observable to positive values without changing order.""" + values = np.asarray(values, dtype=float) + values = values[np.isfinite(values)] + values = np.abs(values) + return normalize(values + 1e-12) + + +def prime_metric_delta_gamma(n_values: int) -> np.ndarray: + primes = sieve_primes_for_count(n_values + 3) + p = primes.astype(float) + tau = np.log(p) + metric = (p / 2.0) ** 2 + dg = np.diff(metric) + dtau = np.diff(tau) + mid = (metric[:-1] + metric[1:]) / 2.0 + gamma = dg / (2.0 * mid * dtau) + delta_gamma = np.diff(gamma) + return positive_bridge_values(delta_gamma[:n_values]) + + +def prime_metric_dR(n_values: int) -> np.ndarray: + primes = sieve_primes_for_count(n_values + 3) + seq = primes.astype(float) + t = np.log(seq) + a = seq / 2.0 + dt = np.diff(t) + dt_mid = (dt[:-1] + dt[1:]) / 2.0 + da = np.diff(a) + a_prime = da / dt + da_prime = np.diff(a_prime) + a_double_prime = da_prime / dt_mid + r_n = 2.0 * a_double_prime / a[1:-1] + d_r = r_n - 2.0 + return positive_bridge_values(d_r[:n_values]) + + +def load_zeta_zeros(n_zeros: int) -> np.ndarray: + zeros_file = DATA_DIR / "odlyzko_cache" / "zeros1.txt" + if not zeros_file.exists(): + raise RuntimeError(f"{zeros_file} not found") + zeros: list[float] = [] + with zeros_file.open() as f: + for line in f: + line = line.strip() + if not line: + continue + zeros.append(float(line)) + if len(zeros) >= n_zeros: + break + if len(zeros) < n_zeros: + raise RuntimeError(f"only {len(zeros)} zeta zeros available, need {n_zeros}") + return np.array(zeros, dtype=float) + + +def dnd_map_trajectory(x0: float, n_iter: int) -> np.ndarray: + x = float(x0) + traj = [x] + for _ in range(n_iter): + if abs(x) < 1e-15: + break + x = 1.0 + 1.0 / x + if not np.isfinite(x): + break + traj.append(x) + return np.array(traj, dtype=float) + + +def zeta_trace_residual(n_values: int, step: int = 5) -> np.ndarray: + zeros = load_zeta_zeros(n_values) + residuals = [] + for x0 in zeros: + traj = dnd_map_trajectory(float(x0), max(step + 2, 15)) + if len(traj) <= step: + continue + linear = PHI + (float(x0) - PHI) * (LAMBDA**step) + residuals.append(traj[step] - linear) + return positive_bridge_values(np.array(residuals[:n_values], dtype=float)) + + +def hydrogen_bound_level_spacings(n_values: int) -> np.ndarray: + # Atomic units: E_n = -1/(2n^2). Positive adjacent spacings shrink smoothly. + n = np.arange(1, n_values + 2, dtype=float) + energy = -1.0 / (2.0 * n**2) + spacings = np.diff(energy) + return normalize(spacings) + + +def build_sequences(args: argparse.Namespace) -> dict[str, np.ndarray]: + return { + "prime_metric_delta_gamma_abs": prime_metric_delta_gamma(args.n_gaps), + "prime_metric_dR_abs": prime_metric_dR(args.n_gaps), + "zeta_trace_residual_step5_abs": zeta_trace_residual(args.zeta_values, step=5), + "hydrogen_bound_level_spacings": hydrogen_bound_level_spacings(args.n_gaps), + } + + +def run(args: argparse.Namespace) -> dict: + root_rng = np.random.default_rng(args.seed) + sequences = build_sequences(args) + perimeters = {} + for name, base in sequences.items(): + perimeters[name] = analyze_sequence(name, base, args, root_rng) + + output = { + "experiment": "bridge_order_denominator_gate", + "category": "gate_falsification_bridge", + "question": "Does ORDER_DENOMINATOR_GATE survive on D-ND bridge sequences beyond prime/zeta/logistic gaps?", + "observables_registry": OBSERVABLES_REGISTRY_VERSION, + "observables_used": OBS_NAMES, + "params": vars(args), + "matrix": compact(perimeters), + "perimeters": perimeters, + } + + out_path = Path(args.out) + out_path.parent.mkdir(parents=True, exist_ok=True) + with out_path.open("w") as f: + json.dump(output, f, indent=2) + + print(f"observables_registry={OBSERVABLES_REGISTRY_VERSION}") + print(f"observables_used={OBS_NAMES}") + print("perimeter n one_sided stable0 stable1 dist_gate ambiguous_gate") + for name, row in output["matrix"].items(): + print( + f"{name:>34s} " + f"{row['n_gaps']:>5d} " + f"{','.join(row['coherent_one_sided_observables']) or '[]':>22s} " + f"{row['stable_count_coherent']:>7.3f} " + f"{row['stable_count_illusory']:>7.3f} " + f"{row['endpoint_distance_one_sided_gated']:>9.3f} " + f"{row['ambiguous_beta_one_sided_gated']}" + ) + print(f"saved {out_path}") + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--n-gaps", type=int, default=4096) + parser.add_argument("--zeta-values", type=int, default=2000) + parser.add_argument("--n-replicates", type=int, default=20) + parser.add_argument("--n-beta", type=int, default=11) + parser.add_argument("--n-baseline", type=int, default=32) + parser.add_argument("--z-min", type=float, default=2.0) + parser.add_argument("--seed", type=int, default=202605070942) + parser.add_argument("--out", default="tools/data/bridge_order_denominator_gate_20260507_0942.json") + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() succeeded in 1969ms: observables_registry=1.0.0-2026-05-06 observables_used=['SR', 'SR2', 'L1', 'L2', 'triple_var'] perimeter n one_sided stable0 stable1 dist_gate ambiguous_gate prime_metric_delta_gamma_abs 4096 SR,SR2,L1,L2,triple_var 5.000 0.167 4.377 [] prime_metric_dR_abs 4096 SR,SR2,L1,L2,triple_var 5.000 0.083 4.376 [] zeta_trace_residual_step5_abs 2000 SR,SR2,L1,L2,triple_var 5.000 0.083 4.377 [0.30000000000000004] hydrogen_bound_level_spacings 4096 SR,SR2,L1,L2,triple_var 5.000 0.250 4.378 [0.4, 0.5] saved tools/data/bridge_order_denominator_gate_20260507_0942_seedcheck.json diff --git a/tools/exp_bridge_order_denominator_gate.py b/tools/exp_bridge_order_denominator_gate.py new file mode 100644 index 0000000000000000000000000000000000000000..17286d6199454f089722ec1e0e6bad01f25f3b92 --- /dev/null +++ b/tools/exp_bridge_order_denominator_gate.py @@ -0,0 +1,206 @@ +#!/usr/bin/env python3 +""" +exp_bridge_order_denominator_gate.py + +Falsification attempt for ORDER_DENOMINATOR_GATE on bridge/perimeter sequences +already present in the D-ND lab context: + +- prime metric connection fluctuations from g=(p/2)^2 +- prime metric curvature fluctuations dR +- zeta trace-bridge nonlinear residuals +- hydrogen bound-level spacings from the QxE bridge + +The coherent endpoint is the observed/generated bridge order. The illusory +endpoint is a marginal-preserving permutation. Canonical observables come from +observables_registry.py. +""" + +from __future__ import annotations + +import argparse +import json +import math +from pathlib import Path + +import numpy as np + +from exp_semireal_order_denominator_gate import analyze_sequence, compact, normalize +from observables_registry import OBSERVABLES_REGISTRY_VERSION, OBSERVABLES_CANONICAL + + +OBS_NAMES = list(OBSERVABLES_CANONICAL.keys()) +PHI = (1.0 + math.sqrt(5.0)) / 2.0 +LAMBDA = -1.0 / PHI**2 +DATA_DIR = Path(__file__).parent / "data" + + +def sieve_primes_for_count(n_primes: int) -> np.ndarray: + if n_primes < 6: + limit = 20 + else: + limit = int(n_primes * (math.log(n_primes) + math.log(math.log(n_primes))) * 1.35) + while True: + sieve = np.ones(limit + 1, dtype=bool) + sieve[:2] = False + for p in range(2, int(limit**0.5) + 1): + if sieve[p]: + sieve[p * p : limit + 1 : p] = False + primes = np.flatnonzero(sieve) + if len(primes) >= n_primes: + return primes[:n_primes].astype(float) + limit *= 2 + + +def positive_bridge_values(values: np.ndarray) -> np.ndarray: + """Map a signed bridge observable to positive values without changing order.""" + values = np.asarray(values, dtype=float) + values = values[np.isfinite(values)] + values = np.abs(values) + return normalize(values + 1e-12) + + +def prime_metric_delta_gamma(n_values: int) -> np.ndarray: + primes = sieve_primes_for_count(n_values + 3) + p = primes.astype(float) + tau = np.log(p) + metric = (p / 2.0) ** 2 + dg = np.diff(metric) + dtau = np.diff(tau) + mid = (metric[:-1] + metric[1:]) / 2.0 + gamma = dg / (2.0 * mid * dtau) + delta_gamma = np.diff(gamma) + return positive_bridge_values(delta_gamma[:n_values]) + + +def prime_metric_dR(n_values: int) -> np.ndarray: + primes = sieve_primes_for_count(n_values + 3) + seq = primes.astype(float) + t = np.log(seq) + a = seq / 2.0 + dt = np.diff(t) + dt_mid = (dt[:-1] + dt[1:]) / 2.0 + da = np.diff(a) + a_prime = da / dt + da_prime = np.diff(a_prime) + a_double_prime = da_prime / dt_mid + r_n = 2.0 * a_double_prime / a[1:-1] + d_r = r_n - 2.0 + return positive_bridge_values(d_r[:n_values]) + + +def load_zeta_zeros(n_zeros: int) -> np.ndarray: + zeros_file = DATA_DIR / "odlyzko_cache" / "zeros1.txt" + if not zeros_file.exists(): + raise RuntimeError(f"{zeros_file} not found") + zeros: list[float] = [] + with zeros_file.open() as f: + for line in f: + line = line.strip() + if not line: + continue + zeros.append(float(line)) + if len(zeros) >= n_zeros: + break + if len(zeros) < n_zeros: + raise RuntimeError(f"only {len(zeros)} zeta zeros available, need {n_zeros}") + return np.array(zeros, dtype=float) + + +def dnd_map_trajectory(x0: float, n_iter: int) -> np.ndarray: + x = float(x0) + traj = [x] + for _ in range(n_iter): + if abs(x) < 1e-15: + break + x = 1.0 + 1.0 / x + if not np.isfinite(x): + break + traj.append(x) + return np.array(traj, dtype=float) + + +def zeta_trace_residual(n_values: int, step: int = 5) -> np.ndarray: + zeros = load_zeta_zeros(n_values) + residuals = [] + for x0 in zeros: + traj = dnd_map_trajectory(float(x0), max(step + 2, 15)) + if len(traj) <= step: + continue + linear = PHI + (float(x0) - PHI) * (LAMBDA**step) + residuals.append(traj[step] - linear) + return positive_bridge_values(np.array(residuals[:n_values], dtype=float)) + + +def hydrogen_bound_level_spacings(n_values: int) -> np.ndarray: + # Atomic units: E_n = -1/(2n^2). Positive adjacent spacings shrink smoothly. + n = np.arange(1, n_values + 2, dtype=float) + energy = -1.0 / (2.0 * n**2) + spacings = np.diff(energy) + return normalize(spacings) + + +def build_sequences(args: argparse.Namespace) -> dict[str, np.ndarray]: + return { + "prime_metric_delta_gamma_abs": prime_metric_delta_gamma(args.n_gaps), + "prime_metric_dR_abs": prime_metric_dR(args.n_gaps), + "zeta_trace_residual_step5_abs": zeta_trace_residual(args.zeta_values, step=5), + "hydrogen_bound_level_spacings": hydrogen_bound_level_spacings(args.n_gaps), + } + + +def run(args: argparse.Namespace) -> dict: + root_rng = np.random.default_rng(args.seed) + sequences = build_sequences(args) + perimeters = {} + for name, base in sequences.items(): + perimeters[name] = analyze_sequence(name, base, args, root_rng) + + output = { + "experiment": "bridge_order_denominator_gate", + "category": "gate_falsification_bridge", + "question": "Does ORDER_DENOMINATOR_GATE survive on D-ND bridge sequences beyond prime/zeta/logistic gaps?", + "observables_registry": OBSERVABLES_REGISTRY_VERSION, + "observables_used": OBS_NAMES, + "params": vars(args), + "matrix": compact(perimeters), + "perimeters": perimeters, + } + + out_path = Path(args.out) + out_path.parent.mkdir(parents=True, exist_ok=True) + with out_path.open("w") as f: + json.dump(output, f, indent=2) + + print(f"observables_registry={OBSERVABLES_REGISTRY_VERSION}") + print(f"observables_used={OBS_NAMES}") + print("perimeter n one_sided stable0 stable1 dist_gate ambiguous_gate") + for name, row in output["matrix"].items(): + print( + f"{name:>34s} " + f"{row['n_gaps']:>5d} " + f"{','.join(row['coherent_one_sided_observables']) or '[]':>22s} " + f"{row['stable_count_coherent']:>7.3f} " + f"{row['stable_count_illusory']:>7.3f} " + f"{row['endpoint_distance_one_sided_gated']:>9.3f} " + f"{row['ambiguous_beta_one_sided_gated']}" + ) + print(f"saved {out_path}") + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--n-gaps", type=int, default=4096) + parser.add_argument("--zeta-values", type=int, default=2000) + parser.add_argument("--n-replicates", type=int, default=20) + parser.add_argument("--n-beta", type=int, default=11) + parser.add_argument("--n-baseline", type=int, default=32) + parser.add_argument("--z-min", type=float, default=2.0) + parser.add_argument("--seed", type=int, default=202605070942) + parser.add_argument("--out", default="tools/data/bridge_order_denominator_gate_20260507_0942.json") + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() codex Il deposito è molto netto: sui quattro perimetri bridge il supporto è one-sided su tutti e cinque gli osservabili canonici. Controllo ora i dettagli numerici e i file modificati, poi scrivo il report con il contro-linguaggio corretto: forte supporto nel perimetro, non universalità del gate. exec /bin/bash -lc 'git status --short' in /opt/MM_D-ND succeeded in 0ms: M applications/INDEX.draft.json M applications/soluzioni/20260430_0330_three-regimes-of-modular-memory-algebraic/finding_index.draft.json M applications/soluzioni/20260430_0330_three-regimes-of-modular-memory-algebraic/manifest.draft.json M tools/LAB_AGENT_CONTEXT.md M tools/data/agent_field_live.md M tools/data/conoscenza_teorie.json M tools/data/consecutio.json M tools/data/consecutio_processata.json M tools/data/domande_fondamentali.json M tools/data/incrocio_risultato.json M tools/data/lab_data.json M tools/data/lab_graph.json M tools/data/lab_health.json M tools/data/lab_session_log.jsonl M tools/data/pipeline_state.json M tools/data/ponti_evoluti.json M tools/data/refresh_detector_state.json M tools/data/seme.json D tools/data/seme_archive/piano_37.json D tools/data/seme_archive/piano_38.json D tools/data/seme_archive/piano_4.json D tools/data/seme_archive/piano_5.json D tools/data/seme_archive/piano_6.json M tools/data/seme_axioms.json M tools/data/seme_backup_pre_run.json M tools/data/tm1_figures/tensions.json M tools/data/tm1_figures/tensions_raw.json ?? applications/published/20260504_0901_the-two-markov-layers-are-coupled/ ?? applications/published/20260504_1219_markov-layers-pass-the-first-recovery/ ?? applications/published/20260505_0330_observable-rank-audit-many-probes-one/ ?? applications/published/20260506_1955_observable-collinearity-breaks-only-where-denominators/ ?? applications/published/20260507_0330_the-gue-poisson-boundary-is-a/ ?? applications/published/20260507_0803_denominator-gate-transfers-boundary-coordinate-does/ ?? applications/scoperte/20260502_0330_the-dipolar-phase-transition-is-nearly_auto/ ?? applications/scoperte/20260503_0330_markov-memory-has-two-visible-layers_auto/ ?? applications/scoperte/20260504_0901_the-two-markov-layers-are-coupled_auto/ ?? applications/scoperte/20260504_1219_markov-layers-pass-the-first-recovery_auto/ ?? applications/scoperte/20260505_0330_observable-rank-audit-many-probes-one_auto/ ?? applications/scoperte/20260506_0625_perturbation-dimensionality-is-not-yet-a_auto/ ?? applications/scoperte/20260506_1941_perturbation-rank-needs-denominator-gating_auto/ ?? applications/scoperte/20260506_1955_observable-collinearity-breaks-only-where-denominators_auto/ ?? applications/scoperte/20260507_0330_the-gue-poisson-boundary-is-a_auto/ ?? applications/scoperte/20260507_0803_denominator-gate-transfers-boundary-coordinate-does_auto/ ?? applications/scoperte/20260507_0901_denominator-gate-transfer-matrix_auto/ ?? applications/scoperte/20260507_0923_semi-real-order-denominator-gate_auto/ ?? applications/soluzioni/20260502_0330_the-dipolar-phase-transition-is-nearly/ ?? applications/soluzioni/20260503_0330_markov-memory-has-two-visible-layers/ ?? applications/soluzioni/20260504_0901_the-two-markov-layers-are-coupled/ ?? applications/soluzioni/20260504_1219_markov-layers-pass-the-first-recovery/ ?? applications/soluzioni/20260505_0330_observable-rank-audit-many-probes-one/ ?? applications/soluzioni/20260506_1941_perturbation-rank-needs-denominator-gating/ ?? applications/soluzioni/20260506_1955_observable-collinearity-breaks-only-where-denominators/ ?? applications/soluzioni/20260507_0330_the-gue-poisson-boundary-is-a/ ?? applications/soluzioni/20260507_0803_denominator-gate-transfers-boundary-coordinate-does/ ?? findings_gallery.md ?? tools/data/.last_telegram_msg ?? tools/data/3d_boundary_layers.json ?? tools/data/aeternitas/ ?? tools/data/alignment_active.json ?? tools/data/alignment_markers.jsonl ?? tools/data/bicono_projections.jsonl ?? tools/data/boundary_coherence.json ?? tools/data/boundary_mixture_gate_20260507_0330.json ?? tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json ?? tools/data/boundary_shuffle_audit.json ?? tools/data/bridge_order_denominator_gate_20260507_0942.json ?? tools/data/bridge_order_denominator_gate_20260507_0942_seedcheck.json ?? tools/data/brody_calibration_results.json ?? tools/data/brody_flow.json ?? tools/data/conoscenza_teorie.json.bak.retraction_22_04 ?? tools/data/cross_domain_dipolar_direction.json ?? tools/data/cross_observable_consistency.json ?? tools/data/crossover_phase_test.json ?? tools/data/denominator_gate_transfer_matrix.json ?? tools/data/dipolar_crossover.json ?? tools/data/dipolar_vector_scaling.json ?? tools/data/domandatore/domandatore_20260421_0746.json ?? tools/data/domandatore/domandatore_20260422_0345.json ?? tools/data/domandatore/domandatore_20260423_0345.json ?? tools/data/domandatore/domandatore_20260424_0345.json ?? tools/data/domandatore/domandatore_20260425_0345.json ?? tools/data/domandatore/domandatore_20260426_0345.json ?? tools/data/domandatore/domandatore_20260427_0345.json ?? tools/data/domandatore/domandatore_20260428_0345.json ?? tools/data/domandatore/domandatore_20260428_1236.json ?? tools/data/domandatore/domandatore_20260429_0345.json ?? tools/data/domandatore/domandatore_20260430_0345.json ?? tools/data/domandatore/domandatore_20260501_0345.json ?? tools/data/domandatore/domandatore_20260502_0345.json ?? tools/data/domandatore/domandatore_20260503_0345.json ?? tools/data/domandatore/domandatore_20260504_0345.json ?? tools/data/domandatore/domandatore_20260505_0345.json ?? tools/data/domandatore/domandatore_20260506_0345.json ?? tools/data/domandatore/domandatore_20260507_0345.json ?? tools/data/domandatore/domandatore_20260507_0728.json ?? tools/data/domandatore/domandatore_20260507_0742.json ?? tools/data/duality_gate_transfer_20260507_0803.json ?? tools/data/duality_gate_transfer_20260507_0803_seedcheck.json ?? tools/data/evolution/evolution_20260422_0330.md ?? tools/data/evolution/evolution_20260422_1616.md ?? tools/data/evolution/evolution_20260423_0330.md ?? tools/data/evolution/evolution_20260424_0330.md ?? tools/data/evolution/evolution_20260425_0330.md ?? tools/data/evolution/evolution_20260427_0330.md ?? tools/data/evolution/evolution_20260428_0330.md ?? tools/data/evolution/evolution_20260429_0852.md ?? tools/data/evolution/evolution_20260429_1013.md ?? tools/data/evolution/evolution_20260430_0330.md ?? tools/data/evolution/evolution_20260430_1905.md ?? tools/data/evolution/evolution_20260430_1946.md ?? tools/data/evolution/evolution_20260501_0330.md ?? tools/data/evolution/evolution_20260501_0725.md ?? tools/data/evolution/evolution_20260501_0931.md ?? tools/data/evolution/evolution_20260502_0330.md ?? tools/data/evolution/evolution_20260503_0330.md ?? tools/data/evolution/evolution_20260504_0901.md ?? tools/data/evolution/evolution_20260504_1219.md ?? tools/data/evolution/evolution_20260505_0330.md ?? tools/data/evolution/evolution_20260506_0330.md ?? tools/data/evolution/evolution_20260506_1941.md ?? tools/data/evolution/evolution_20260506_1955.md ?? tools/data/evolution/evolution_20260507_0330.md ?? tools/data/evolution/evolution_20260507_0803.md ?? tools/data/evolution/evolution_20260507_0901.md ?? tools/data/evolution/evolution_20260507_0923.md ?? tools/data/incrocio_20260422_0336.json ?? tools/data/incrocio_20260423_0335.json ?? tools/data/incrocio_20260424_0347.json ?? tools/data/incrocio_20260425_0339.json ?? tools/data/incrocio_20260428_0340.json ?? tools/data/incrocio_20260429_0859.json ?? tools/data/incrocio_20260506_0633.json ?? tools/data/lab_bridge_issues.jsonl ?? tools/data/magnitude_psd_from_acf.json ?? tools/data/markov3_observable_hunt.json ?? tools/data/markov_dipolar_decomposition.json ?? tools/data/markov_k_direction.json ?? tools/data/markov_layer_recovery_audit.json ?? tools/data/markov_memory_by_gue_type.json ?? tools/data/markov_scale_function.json ?? tools/data/meta_tautology_test.json ?? tools/data/mod3_scaling.json ?? tools/data/mod3_vs_residual_ordering.json ?? tools/data/modular_algebra_depth.json ?? tools/data/modular_memory_spectrum.json ?? tools/data/observable_collinearity_breaking_20260506_1955.json ?? tools/data/observable_collinearity_breaking_20260506_1956.json ?? tools/data/observable_collinearity_breaking_20260506_1957.json ?? tools/data/observable_rank_audit.json ?? tools/data/observable_rank_audit_seed20260506.json ?? tools/data/perturbation_dimensionality_audit.json ?? tools/data/perturbation_dimensionality_audit_scale0330.json ?? tools/data/perturbation_rank_size_curve.json ?? tools/data/promotions/ ?? tools/data/reports/_quarantine_falsifier_29_04/ ?? tools/data/reports/agent_20260422_0330.md ?? tools/data/reports/agent_20260422_1616.md ?? tools/data/reports/agent_20260423_0330.md ?? tools/data/reports/agent_20260424_0330.md ?? tools/data/reports/agent_20260425_0330.md ?? tools/data/reports/agent_20260426_0330.md ?? tools/data/reports/agent_20260427_0330.md ?? tools/data/reports/agent_20260428_0330.md ?? tools/data/reports/agent_20260429_1013.md ?? tools/data/reports/agent_20260429_1041.md ?? tools/data/reports/agent_20260430_0330.md ?? tools/data/reports/agent_20260430_1905.md ?? tools/data/reports/agent_20260430_1919.md ?? tools/data/reports/agent_20260430_1946.md ?? tools/data/reports/agent_20260501_0330.md ?? tools/data/reports/agent_20260501_0725.md ?? tools/data/reports/agent_20260501_0931.md ?? tools/data/reports/agent_20260502_0330.md ?? tools/data/reports/agent_20260503_0330.md ?? tools/data/reports/agent_20260504_0901.md ?? tools/data/reports/agent_20260504_1219.md ?? tools/data/reports/agent_20260505_0330.md ?? tools/data/reports/agent_20260505_1022.md ?? tools/data/reports/agent_20260506_0330.md ?? tools/data/reports/agent_20260506_0625.md ?? tools/data/reports/agent_20260506_1941.md ?? tools/data/reports/agent_20260506_1955.md ?? tools/data/reports/agent_20260507_0330.md ?? tools/data/reports/agent_20260507_0803.md ?? tools/data/reports/agent_20260507_0901.md ?? tools/data/reports/agent_20260507_0923.md ?? tools/data/reports/ddf_20260422_0405.json ?? tools/data/reports/ddf_20260423_0405.json ?? tools/data/reports/ddf_20260424_0405.json ?? tools/data/reports/ddf_20260425_0405.json ?? tools/data/reports/ddf_20260426_0405.json ?? tools/data/reports/ddf_20260427_0405.json ?? tools/data/reports/ddf_20260428_0405.json ?? tools/data/reports/ddf_20260429_0405.json ?? tools/data/reports/ddf_20260430_0405.json ?? tools/data/reports/ddf_20260501_0405.json ?? tools/data/reports/ddf_20260502_0405.json ?? tools/data/reports/ddf_20260503_0405.json ?? tools/data/reports/ddf_20260504_0405.json ?? tools/data/reports/ddf_20260505_0405.json ?? tools/data/reports/ddf_20260505_0636.json ?? tools/data/reports/ddf_20260506_0405.json ?? tools/data/reports/ddf_20260507_0405.json ?? tools/data/reports/evolution_20260422_0330.md ?? tools/data/reports/evolution_20260422_1616.md ?? tools/data/reports/evolution_20260423_0330.md ?? tools/data/reports/evolution_20260424_0330.md ?? tools/data/reports/evolution_20260425_0330.md ?? tools/data/reports/evolution_20260427_0330.md ?? tools/data/reports/evolution_20260428_0330.md ?? tools/data/reports/evolution_20260503_0330.md ?? tools/data/reports/evolution_20260504_0330.md ?? tools/data/reports/evolution_20260505_0330.md ?? tools/data/reports/evolution_20260506_0330.md ?? tools/data/reports/evolution_20260506_1941.md ?? tools/data/reports/falsifier_20260429_1013.json ?? tools/data/reports/falsifier_20260429_1041.json ?? tools/data/reports/falsifier_20260430_0330.json ?? tools/data/reports/falsifier_20260430_1905.json ?? tools/data/reports/falsifier_20260430_1919.json ?? tools/data/reports/falsifier_20260430_1946.json ?? tools/data/reports/falsifier_20260501_0330.json ?? tools/data/reports/falsifier_20260501_0725.json ?? tools/data/reports/falsifier_20260501_0931.json ?? tools/data/reports/falsifier_20260502_0330.json ?? tools/data/reports/falsifier_20260503_0330.json ?? tools/data/reports/falsifier_20260504_0901.json ?? tools/data/reports/falsifier_20260504_1219.json ?? tools/data/reports/falsifier_20260505_0330.json ?? tools/data/reports/falsifier_20260506_0330.raw.txt ?? tools/data/reports/falsifier_20260506_0625.json ?? tools/data/reports/falsifier_20260506_1941.json ?? tools/data/reports/falsifier_20260506_1955.json ?? tools/data/reports/falsifier_20260506_1955.raw.txt ?? tools/data/reports/falsifier_20260507_0330.json ?? tools/data/reports/falsifier_20260507_0330.raw.txt ?? tools/data/reports/falsifier_20260507_0803.json ?? tools/data/reports/falsifier_20260507_0803.raw.txt ?? tools/data/reports/falsifier_20260507_0901.json ?? tools/data/reports/falsifier_20260507_0923.json ?? tools/data/reports/falsifier_20260507_0923.raw.txt ?? tools/data/reports/incident_20260504_0721.md ?? tools/data/reports/incident_20260504_1138.md ?? tools/data/reports/latest.md ?? tools/data/reports/loop_guard_20260507_0330.json ?? tools/data/reports/loop_guard_20260507_0803.json ?? tools/data/reports/loop_guard_20260507_0901.json ?? tools/data/reports/loop_guard_20260507_0923.json ?? tools/data/reports/trajectory_apply_20260506_1931.json ?? tools/data/reports/trajectory_apply_20260506_1941.json ?? tools/data/reports/trajectory_apply_20260506_1955.json ?? tools/data/reports/trajectory_apply_20260507_0330.json ?? tools/data/reports/trajectory_apply_20260507_0803.json ?? tools/data/reports/trajectory_apply_20260507_0901.json ?? tools/data/reports/trajectory_apply_20260507_0923.json ?? tools/data/reports/trajectory_apply_20260507_0942.json ?? tools/data/scale_selective_perturbation.json ?? tools/data/selective_layer_decoupling.json ?? tools/data/seme_archive/piano_67.json ?? tools/data/seme_archive/piano_68.json ?? tools/data/seme_archive/piano_69.json ?? tools/data/seme_archive/piano_70.json ?? tools/data/seme_archive/piano_71.json ?? tools/data/semireal_order_denominator_gate_20260507_0923.json ?? tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json ?? tools/data/spectral_rigidity_results.json ?? tools/data/trajectory_apply_history.jsonl ?? tools/data/two_channel_boundary.json ?? tools/data/two_channel_cross_domain.json ?? tools/data/two_channel_shuffle_audit.json ?? tools/data/two_layer_universality.json ?? tools/data/valutatore_log.jsonl ?? tools/data/veritas/ ?? tools/evolution_report.md ?? tools/exp_3d_boundary_layers.py ?? tools/exp_boundary_coherence.py ?? tools/exp_boundary_mixture_gate.py ?? tools/exp_boundary_shuffle_audit.py ?? tools/exp_bridge_order_denominator_gate.py ?? tools/exp_brody_calibration.py ?? tools/exp_brody_flow.py ?? tools/exp_cross_domain_dipolar_direction.py ?? tools/exp_cross_observable_consistency.py ?? tools/exp_crossover_phase_test.py ?? tools/exp_denominator_gate_transfer_matrix.py ?? tools/exp_dipolar_angle_reference.py ?? tools/exp_dipolar_crossover.py ?? tools/exp_dipolar_vector_scaling.py ?? tools/exp_duality_gate_transfer.py ?? tools/exp_magnitude_psd_from_acf.py ?? tools/exp_markov3_observable_hunt.py ?? tools/exp_markov_dipolar_decomposition.py ?? tools/exp_markov_k_direction.py ?? tools/exp_markov_layer_recovery_audit.py ?? tools/exp_markov_memory_by_gue_type.py ?? tools/exp_markov_psd_prediction.py ?? tools/exp_markov_scale_function.py ?? tools/exp_meta_tautology_test.py ?? tools/exp_mod3_scaling.py ?? tools/exp_mod3_vs_residual_ordering.py ?? tools/exp_modular_algebra_depth.py ?? tools/exp_modular_memory_spectrum.py ?? tools/exp_observable_rank_audit.py ?? tools/exp_perturbation_dimensionality_audit.py ?? tools/exp_perturbation_rank_size_curve.py ?? tools/exp_scale_selective_perturbation.py ?? tools/exp_selective_layer_decoupling.py ?? tools/exp_semireal_order_denominator_gate.py ?? tools/exp_spectral_rigidity.py ?? tools/exp_two_channel_boundary.py ?? tools/exp_two_channel_cross_domain.py ?? tools/exp_two_channel_shuffle_audit.py ?? tools/exp_two_layer_universality.py ?? tools/observables_registry.py exec /bin/bash -lc 'python -m py_compile tools/exp_bridge_order_denominator_gate.py' in /opt/MM_D-ND succeeded in 0ms: exec /bin/bash -lc "python - <<'PY' import json for path in ['tools/data/bridge_order_denominator_gate_20260507_0942.json','tools/data/bridge_order_denominator_gate_20260507_0942_seedcheck.json']: d=json.load(open(path)) print(path) for k,v in d['matrix'].items(): print(k) print(' one', v['coherent_one_sided_observables']) print(' endpoint', v['endpoint_stable_observables']) print(' stable', v['stable_count_coherent'], v['stable_count_illusory']) print(' dist', v['endpoint_distance_one_sided_gated'], 'amb', v['ambiguous_beta_one_sided_gated']) print(' z0', {kk: round(vv,3) for kk,vv in v['z_mean_coherent'].items()}) print(' z1', {kk: round(vv,3) for kk,vv in v['z_mean_illusory'].items()}) print() PY" in /opt/MM_D-ND succeeded in 0ms: tools/data/bridge_order_denominator_gate_20260507_0942.json prime_metric_delta_gamma_abs one ['SR', 'SR2', 'L1', 'L2', 'triple_var'] endpoint [] stable 5.0 0.25 dist 4.414635336941564 amb [] z0 {'SR': 67.336, 'SR2': 29.352, 'L1': 317.366, 'L2': 208.745, 'triple_var': 209.919} z1 {'SR': 0.107, 'SR2': 0.262, 'L1': 0.441, 'L2': -0.182, 'triple_var': 0.257} prime_metric_dR_abs one ['SR', 'SR2', 'L1', 'L2', 'triple_var'] endpoint [] stable 5.0 0.35 dist 4.413575630785063 amb [0.30000000000000004] z0 {'SR': 51.958, 'SR2': 28.133, 'L1': 66.145, 'L2': 56.642, 'triple_var': 76.499} z1 {'SR': -0.198, 'SR2': 0.179, 'L1': 0.296, 'L2': -0.127, 'triple_var': 0.031} zeta_trace_residual_step5_abs one ['SR', 'SR2', 'L1', 'L2', 'triple_var'] endpoint [] stable 5.0 0.05 dist 4.415027382225652 amb [0.30000000000000004] z0 {'SR': 99.03, 'SR2': 102.227, 'L1': 45.234, 'L2': 46.548, 'triple_var': 62.457} z1 {'SR': -0.269, 'SR2': 0.497, 'L1': -0.419, 'L2': 0.325, 'triple_var': -0.219} hydrogen_bound_level_spacings one ['SR', 'SR2', 'L1', 'L2', 'triple_var'] endpoint [] stable 5.0 0.2 dist 4.415830202414151 amb [0.4] z0 {'SR': 196.203, 'SR2': 203.96, 'L1': 15015.574, 'L2': 2183.603, 'triple_var': -3699.374} z1 {'SR': -0.021, 'SR2': -0.087, 'L1': -0.132, 'L2': -0.23, 'triple_var': -0.223} tools/data/bridge_order_denominator_gate_20260507_0942_seedcheck.json prime_metric_delta_gamma_abs one ['SR', 'SR2', 'L1', 'L2', 'triple_var'] endpoint [] stable 5.0 0.16666666666666666 dist 4.376601290446516 amb [] z0 {'SR': 78.01, 'SR2': 28.591, 'L1': 390.359, 'L2': 200.762, 'triple_var': 175.304} z1 {'SR': 0.153, 'SR2': 0.481, 'L1': -0.246, 'L2': 0.264, 'triple_var': -0.304} prime_metric_dR_abs one ['SR', 'SR2', 'L1', 'L2', 'triple_var'] endpoint [] stable 5.0 0.08333333333333333 dist 4.376011611886738 amb [] z0 {'SR': 48.469, 'SR2': 29.193, 'L1': 69.022, 'L2': 63.63, 'triple_var': 78.457} z1 {'SR': -0.027, 'SR2': -0.013, 'L1': -0.055, 'L2': 0.08, 'triple_var': -0.104} zeta_trace_residual_step5_abs one ['SR', 'SR2', 'L1', 'L2', 'triple_var'] endpoint [] stable 5.0 0.08333333333333333 dist 4.376766340552007 amb [0.30000000000000004] z0 {'SR': 99.447, 'SR2': 98.302, 'L1': 45.977, 'L2': 45.324, 'triple_var': 61.61} z1 {'SR': 0.102, 'SR2': 0.069, 'L1': 0.329, 'L2': 0.194, 'triple_var': 0.32} hydrogen_bound_level_spacings one ['SR', 'SR2', 'L1', 'L2', 'triple_var'] endpoint [] stable 5.0 0.25 dist 4.377935861372162 amb [0.4, 0.5] z0 {'SR': 213.675, 'SR2': 195.108, 'L1': 136743.094, 'L2': 38935.005, 'triple_var': -41689.228} z1 {'SR': 0.395, 'SR2': -0.397, 'L1': -0.008, 'L2': -0.02, 'triple_var': 0.047} diff --git a/tools/exp_bridge_order_denominator_gate.py b/tools/exp_bridge_order_denominator_gate.py new file mode 100644 index 0000000000000000000000000000000000000000..17286d6199454f089722ec1e0e6bad01f25f3b92 --- /dev/null +++ b/tools/exp_bridge_order_denominator_gate.py @@ -0,0 +1,206 @@ +#!/usr/bin/env python3 +""" +exp_bridge_order_denominator_gate.py + +Falsification attempt for ORDER_DENOMINATOR_GATE on bridge/perimeter sequences +already present in the D-ND lab context: + +- prime metric connection fluctuations from g=(p/2)^2 +- prime metric curvature fluctuations dR +- zeta trace-bridge nonlinear residuals +- hydrogen bound-level spacings from the QxE bridge + +The coherent endpoint is the observed/generated bridge order. The illusory +endpoint is a marginal-preserving permutation. Canonical observables come from +observables_registry.py. +""" + +from __future__ import annotations + +import argparse +import json +import math +from pathlib import Path + +import numpy as np + +from exp_semireal_order_denominator_gate import analyze_sequence, compact, normalize +from observables_registry import OBSERVABLES_REGISTRY_VERSION, OBSERVABLES_CANONICAL + + +OBS_NAMES = list(OBSERVABLES_CANONICAL.keys()) +PHI = (1.0 + math.sqrt(5.0)) / 2.0 +LAMBDA = -1.0 / PHI**2 +DATA_DIR = Path(__file__).parent / "data" + + +def sieve_primes_for_count(n_primes: int) -> np.ndarray: + if n_primes < 6: + limit = 20 + else: + limit = int(n_primes * (math.log(n_primes) + math.log(math.log(n_primes))) * 1.35) + while True: + sieve = np.ones(limit + 1, dtype=bool) + sieve[:2] = False + for p in range(2, int(limit**0.5) + 1): + if sieve[p]: + sieve[p * p : limit + 1 : p] = False + primes = np.flatnonzero(sieve) + if len(primes) >= n_primes: + return primes[:n_primes].astype(float) + limit *= 2 + + +def positive_bridge_values(values: np.ndarray) -> np.ndarray: + """Map a signed bridge observable to positive values without changing order.""" + values = np.asarray(values, dtype=float) + values = values[np.isfinite(values)] + values = np.abs(values) + return normalize(values + 1e-12) + + +def prime_metric_delta_gamma(n_values: int) -> np.ndarray: + primes = sieve_primes_for_count(n_values + 3) + p = primes.astype(float) + tau = np.log(p) + metric = (p / 2.0) ** 2 + dg = np.diff(metric) + dtau = np.diff(tau) + mid = (metric[:-1] + metric[1:]) / 2.0 + gamma = dg / (2.0 * mid * dtau) + delta_gamma = np.diff(gamma) + return positive_bridge_values(delta_gamma[:n_values]) + + +def prime_metric_dR(n_values: int) -> np.ndarray: + primes = sieve_primes_for_count(n_values + 3) + seq = primes.astype(float) + t = np.log(seq) + a = seq / 2.0 + dt = np.diff(t) + dt_mid = (dt[:-1] + dt[1:]) / 2.0 + da = np.diff(a) + a_prime = da / dt + da_prime = np.diff(a_prime) + a_double_prime = da_prime / dt_mid + r_n = 2.0 * a_double_prime / a[1:-1] + d_r = r_n - 2.0 + return positive_bridge_values(d_r[:n_values]) + + +def load_zeta_zeros(n_zeros: int) -> np.ndarray: + zeros_file = DATA_DIR / "odlyzko_cache" / "zeros1.txt" + if not zeros_file.exists(): + raise RuntimeError(f"{zeros_file} not found") + zeros: list[float] = [] + with zeros_file.open() as f: + for line in f: + line = line.strip() + if not line: + continue + zeros.append(float(line)) + if len(zeros) >= n_zeros: + break + if len(zeros) < n_zeros: + raise RuntimeError(f"only {len(zeros)} zeta zeros available, need {n_zeros}") + return np.array(zeros, dtype=float) + + +def dnd_map_trajectory(x0: float, n_iter: int) -> np.ndarray: + x = float(x0) + traj = [x] + for _ in range(n_iter): + if abs(x) < 1e-15: + break + x = 1.0 + 1.0 / x + if not np.isfinite(x): + break + traj.append(x) + return np.array(traj, dtype=float) + + +def zeta_trace_residual(n_values: int, step: int = 5) -> np.ndarray: + zeros = load_zeta_zeros(n_values) + residuals = [] + for x0 in zeros: + traj = dnd_map_trajectory(float(x0), max(step + 2, 15)) + if len(traj) <= step: + continue + linear = PHI + (float(x0) - PHI) * (LAMBDA**step) + residuals.append(traj[step] - linear) + return positive_bridge_values(np.array(residuals[:n_values], dtype=float)) + + +def hydrogen_bound_level_spacings(n_values: int) -> np.ndarray: + # Atomic units: E_n = -1/(2n^2). Positive adjacent spacings shrink smoothly. + n = np.arange(1, n_values + 2, dtype=float) + energy = -1.0 / (2.0 * n**2) + spacings = np.diff(energy) + return normalize(spacings) + + +def build_sequences(args: argparse.Namespace) -> dict[str, np.ndarray]: + return { + "prime_metric_delta_gamma_abs": prime_metric_delta_gamma(args.n_gaps), + "prime_metric_dR_abs": prime_metric_dR(args.n_gaps), + "zeta_trace_residual_step5_abs": zeta_trace_residual(args.zeta_values, step=5), + "hydrogen_bound_level_spacings": hydrogen_bound_level_spacings(args.n_gaps), + } + + +def run(args: argparse.Namespace) -> dict: + root_rng = np.random.default_rng(args.seed) + sequences = build_sequences(args) + perimeters = {} + for name, base in sequences.items(): + perimeters[name] = analyze_sequence(name, base, args, root_rng) + + output = { + "experiment": "bridge_order_denominator_gate", + "category": "gate_falsification_bridge", + "question": "Does ORDER_DENOMINATOR_GATE survive on D-ND bridge sequences beyond prime/zeta/logistic gaps?", + "observables_registry": OBSERVABLES_REGISTRY_VERSION, + "observables_used": OBS_NAMES, + "params": vars(args), + "matrix": compact(perimeters), + "perimeters": perimeters, + } + + out_path = Path(args.out) + out_path.parent.mkdir(parents=True, exist_ok=True) + with out_path.open("w") as f: + json.dump(output, f, indent=2) + + print(f"observables_registry={OBSERVABLES_REGISTRY_VERSION}") + print(f"observables_used={OBS_NAMES}") + print("perimeter n one_sided stable0 stable1 dist_gate ambiguous_gate") + for name, row in output["matrix"].items(): + print( + f"{name:>34s} " + f"{row['n_gaps']:>5d} " + f"{','.join(row['coherent_one_sided_observables']) or '[]':>22s} " + f"{row['stable_count_coherent']:>7.3f} " + f"{row['stable_count_illusory']:>7.3f} " + f"{row['endpoint_distance_one_sided_gated']:>9.3f} " + f"{row['ambiguous_beta_one_sided_gated']}" + ) + print(f"saved {out_path}") + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--n-gaps", type=int, default=4096) + parser.add_argument("--zeta-values", type=int, default=2000) + parser.add_argument("--n-replicates", type=int, default=20) + parser.add_argument("--n-beta", type=int, default=11) + parser.add_argument("--n-baseline", type=int, default=32) + parser.add_argument("--z-min", type=float, default=2.0) + parser.add_argument("--seed", type=int, default=202605070942) + parser.add_argument("--out", default="tools/data/bridge_order_denominator_gate_20260507_0942.json") + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() codex Prima del report aggiorno il seme nel nodo giusto: non aggiungo una tensione parallela, estendo il claim esistente `ORDER_DENOMINATOR_GATE` con il nuovo perimetro bridge. È una riparazione regressiva sul perimetro del claim, non una seconda categoria. exec /bin/bash -lc "rg -n 'ORDER_DENOMINATOR_GATE|agent_20260507_0923' tools/data/seme.json" in /opt/MM_D-ND succeeded in 0ms: 40: "id": "ORDER_DENOMINATOR_GATE", 41: "claim": "Il denominator gate trasferisce come supporto one-sided dell'ordine quando l'ordine e visibile agli osservabili canonici del perimetro, non come endpoint-stable support a due poli. Nel perimetro sintetico agent_20260507_0901, 4/4 domini non-BOUNDARY hanno endpoint_stable_observables=[] e polo coerente stable_count 3.0-5.0. Nel perimetro semi-reale agent_20260507_0923, primi e zeta trasferiscono (primi: SR,L1,triple_var; zeta: SR,L2), ma logistic_return_intervals e blank: stable_count coerente 0.0-0.2. La beta 0.30 e coordinata del protocollo quando compare, non coordinata universale.", 46: "origine": "cycle agent_20260507_0901 + agent_20260507_0923: transfer matrix sintetica e falsificazione semi-reale su primi, zeta, logistic returns", 140: "claim": "Applied valutatore REDESIGN from 20260507_0901: Falsificare ORDER_DENOMINATOR_GATE su domini non-sintetici o semi-reali: applicare il gate one-sided a 2-3 sequenze fisiche/ponte gia' presenti nel sito, con shuffle e surrogati preservanti marginale,", 162: "Tensioni risolte: {'G_POTENZIALE_NULLA', 'DUALITA_DIPOLARE_VS_ILLUSORIA', 'PERTURBATION_DENOMINATOR_GATE', 'ORDER_DENOMINATOR_GATE', 'METRIC_TENSOR', 'TRAJECTORY_APPLY_20260507_0803', 'OBSERVABLE_REGISTRY', 'TENSIONE_ENTITA', 'TRAJECTORY_APPLY_20260507_0901', 'TRASCENDENZA_LIMITE', 'BOUNDARY_LAYER_GATE', 'PIANO_PRIMARIO_DUE_ASSIOMI'}" 169: "direzione": "Falsificare ORDER_DENOMINATOR_GATE su domini non-sintetici o semi-reali: applicare il gate one-sided a 2-3 sequenze fisiche/ponte gia' presenti nel sito, con shuffle e surrogati preservanti marginale,", diff --git a/tools/exp_bridge_order_denominator_gate.py b/tools/exp_bridge_order_denominator_gate.py new file mode 100644 index 0000000000000000000000000000000000000000..17286d6199454f089722ec1e0e6bad01f25f3b92 --- /dev/null +++ b/tools/exp_bridge_order_denominator_gate.py @@ -0,0 +1,206 @@ +#!/usr/bin/env python3 +""" +exp_bridge_order_denominator_gate.py + +Falsification attempt for ORDER_DENOMINATOR_GATE on bridge/perimeter sequences +already present in the D-ND lab context: + +- prime metric connection fluctuations from g=(p/2)^2 +- prime metric curvature fluctuations dR +- zeta trace-bridge nonlinear residuals +- hydrogen bound-level spacings from the QxE bridge + +The coherent endpoint is the observed/generated bridge order. The illusory +endpoint is a marginal-preserving permutation. Canonical observables come from +observables_registry.py. +""" + +from __future__ import annotations + +import argparse +import json +import math +from pathlib import Path + +import numpy as np + +from exp_semireal_order_denominator_gate import analyze_sequence, compact, normalize +from observables_registry import OBSERVABLES_REGISTRY_VERSION, OBSERVABLES_CANONICAL + + +OBS_NAMES = list(OBSERVABLES_CANONICAL.keys()) +PHI = (1.0 + math.sqrt(5.0)) / 2.0 +LAMBDA = -1.0 / PHI**2 +DATA_DIR = Path(__file__).parent / "data" + + +def sieve_primes_for_count(n_primes: int) -> np.ndarray: + if n_primes < 6: + limit = 20 + else: + limit = int(n_primes * (math.log(n_primes) + math.log(math.log(n_primes))) * 1.35) + while True: + sieve = np.ones(limit + 1, dtype=bool) + sieve[:2] = False + for p in range(2, int(limit**0.5) + 1): + if sieve[p]: + sieve[p * p : limit + 1 : p] = False + primes = np.flatnonzero(sieve) + if len(primes) >= n_primes: + return primes[:n_primes].astype(float) + limit *= 2 + + +def positive_bridge_values(values: np.ndarray) -> np.ndarray: + """Map a signed bridge observable to positive values without changing order.""" + values = np.asarray(values, dtype=float) + values = values[np.isfinite(values)] + values = np.abs(values) + return normalize(values + 1e-12) + + +def prime_metric_delta_gamma(n_values: int) -> np.ndarray: + primes = sieve_primes_for_count(n_values + 3) + p = primes.astype(float) + tau = np.log(p) + metric = (p / 2.0) ** 2 + dg = np.diff(metric) + dtau = np.diff(tau) + mid = (metric[:-1] + metric[1:]) / 2.0 + gamma = dg / (2.0 * mid * dtau) + delta_gamma = np.diff(gamma) + return positive_bridge_values(delta_gamma[:n_values]) + + +def prime_metric_dR(n_values: int) -> np.ndarray: + primes = sieve_primes_for_count(n_values + 3) + seq = primes.astype(float) + t = np.log(seq) + a = seq / 2.0 + dt = np.diff(t) + dt_mid = (dt[:-1] + dt[1:]) / 2.0 + da = np.diff(a) + a_prime = da / dt + da_prime = np.diff(a_prime) + a_double_prime = da_prime / dt_mid + r_n = 2.0 * a_double_prime / a[1:-1] + d_r = r_n - 2.0 + return positive_bridge_values(d_r[:n_values]) + + +def load_zeta_zeros(n_zeros: int) -> np.ndarray: + zeros_file = DATA_DIR / "odlyzko_cache" / "zeros1.txt" + if not zeros_file.exists(): + raise RuntimeError(f"{zeros_file} not found") + zeros: list[float] = [] + with zeros_file.open() as f: + for line in f: + line = line.strip() + if not line: + continue + zeros.append(float(line)) + if len(zeros) >= n_zeros: + break + if len(zeros) < n_zeros: + raise RuntimeError(f"only {len(zeros)} zeta zeros available, need {n_zeros}") + return np.array(zeros, dtype=float) + + +def dnd_map_trajectory(x0: float, n_iter: int) -> np.ndarray: + x = float(x0) + traj = [x] + for _ in range(n_iter): + if abs(x) < 1e-15: + break + x = 1.0 + 1.0 / x + if not np.isfinite(x): + break + traj.append(x) + return np.array(traj, dtype=float) + + +def zeta_trace_residual(n_values: int, step: int = 5) -> np.ndarray: + zeros = load_zeta_zeros(n_values) + residuals = [] + for x0 in zeros: + traj = dnd_map_trajectory(float(x0), max(step + 2, 15)) + if len(traj) <= step: + continue + linear = PHI + (float(x0) - PHI) * (LAMBDA**step) + residuals.append(traj[step] - linear) + return positive_bridge_values(np.array(residuals[:n_values], dtype=float)) + + +def hydrogen_bound_level_spacings(n_values: int) -> np.ndarray: + # Atomic units: E_n = -1/(2n^2). Positive adjacent spacings shrink smoothly. + n = np.arange(1, n_values + 2, dtype=float) + energy = -1.0 / (2.0 * n**2) + spacings = np.diff(energy) + return normalize(spacings) + + +def build_sequences(args: argparse.Namespace) -> dict[str, np.ndarray]: + return { + "prime_metric_delta_gamma_abs": prime_metric_delta_gamma(args.n_gaps), + "prime_metric_dR_abs": prime_metric_dR(args.n_gaps), + "zeta_trace_residual_step5_abs": zeta_trace_residual(args.zeta_values, step=5), + "hydrogen_bound_level_spacings": hydrogen_bound_level_spacings(args.n_gaps), + } + + +def run(args: argparse.Namespace) -> dict: + root_rng = np.random.default_rng(args.seed) + sequences = build_sequences(args) + perimeters = {} + for name, base in sequences.items(): + perimeters[name] = analyze_sequence(name, base, args, root_rng) + + output = { + "experiment": "bridge_order_denominator_gate", + "category": "gate_falsification_bridge", + "question": "Does ORDER_DENOMINATOR_GATE survive on D-ND bridge sequences beyond prime/zeta/logistic gaps?", + "observables_registry": OBSERVABLES_REGISTRY_VERSION, + "observables_used": OBS_NAMES, + "params": vars(args), + "matrix": compact(perimeters), + "perimeters": perimeters, + } + + out_path = Path(args.out) + out_path.parent.mkdir(parents=True, exist_ok=True) + with out_path.open("w") as f: + json.dump(output, f, indent=2) + + print(f"observables_registry={OBSERVABLES_REGISTRY_VERSION}") + print(f"observables_used={OBS_NAMES}") + print("perimeter n one_sided stable0 stable1 dist_gate ambiguous_gate") + for name, row in output["matrix"].items(): + print( + f"{name:>34s} " + f"{row['n_gaps']:>5d} " + f"{','.join(row['coherent_one_sided_observables']) or '[]':>22s} " + f"{row['stable_count_coherent']:>7.3f} " + f"{row['stable_count_illusory']:>7.3f} " + f"{row['endpoint_distance_one_sided_gated']:>9.3f} " + f"{row['ambiguous_beta_one_sided_gated']}" + ) + print(f"saved {out_path}") + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--n-gaps", type=int, default=4096) + parser.add_argument("--zeta-values", type=int, default=2000) + parser.add_argument("--n-replicates", type=int, default=20) + parser.add_argument("--n-beta", type=int, default=11) + parser.add_argument("--n-baseline", type=int, default=32) + parser.add_argument("--z-min", type=float, default=2.0) + parser.add_argument("--seed", type=int, default=202605070942) + parser.add_argument("--out", default="tools/data/bridge_order_denominator_gate_20260507_0942.json") + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() apply patch patch: completed /opt/MM_D-ND/tools/data/seme.json diff --git a/tools/data/seme.json b/tools/data/seme.json index 0c6e20e89b979c05b2f3d06089e734d017a0159d..a081e04934a85172c6941c0e2f636bbace866214 --- a/tools/data/seme.json +++ b/tools/data/seme.json @@ -38,12 +38,12 @@ { "tipo": "vincolo", "id": "ORDER_DENOMINATOR_GATE", - "claim": "Il denominator gate trasferisce come supporto one-sided dell'ordine quando l'ordine e visibile agli osservabili canonici del perimetro, non come endpoint-stable support a due poli. Nel perimetro sintetico agent_20260507_0901, 4/4 domini non-BOUNDARY hanno endpoint_stable_observables=[] e polo coerente stable_count 3.0-5.0. Nel perimetro semi-reale agent_20260507_0923, primi e zeta trasferiscono (primi: SR,L1,triple_var; zeta: SR,L2), ma logistic_return_intervals e blank: stable_count coerente 0.0-0.2. La beta 0.30 e coordinata del protocollo quando compare, non coordinata universale.", + "claim": "Il denominator gate trasferisce come supporto one-sided dell'ordine quando l'ordine e visibile agli osservabili canonici del perimetro, non come endpoint-stable support a due poli. Nel perimetro sintetico agent_20260507_0901, 4/4 domini non-BOUNDARY hanno endpoint_stable_observables=[] e polo coerente stable_count 3.0-5.0. Nel perimetro semi-reale agent_20260507_0923, primi e zeta trasferiscono (primi: SR,L1,triple_var; zeta: SR,L2), ma logistic_return_intervals e blank: stable_count coerente 0.0-0.2. Nel perimetro bridge agent_20260507_0942, prime_metric_delta_gamma_abs, prime_metric_dR_abs, zeta_trace_residual_step5_abs e hydrogen_bound_level_spacings trasferiscono su tutti i 5 osservabili canonici con endpoint_stable_observables=[]; e supporto perimetro-bridge, non universalita del gate. La beta 0.30/0.40/0.50 resta coordinata del protocollo quando compare, non coordinata universale.", "intensita": 0.92, "porta": "META", "manuale": true, "condensato_ref": "A4,A8,A14,C2", - "origine": "cycle agent_20260507_0901 + agent_20260507_0923: transfer matrix sintetica e falsificazione semi-reale su primi, zeta, logistic returns", + "origine": "cycle agent_20260507_0901 + agent_20260507_0923 + agent_20260507_0942: transfer matrix sintetica, falsificazione semi-reale su primi/zeta/logistic returns, e perimetri bridge metric/trace/QxE", "added_at": "2026-05-07T09:01:00+00:00" }, { @@ -333,4 +333,4 @@ "timestamp": "2026-04-04T19:34:08.014Z" } ] -} \ No newline at end of file +} diff --git a/tools/exp_bridge_order_denominator_gate.py b/tools/exp_bridge_order_denominator_gate.py new file mode 100644 index 0000000000000000000000000000000000000000..17286d6199454f089722ec1e0e6bad01f25f3b92 --- /dev/null +++ b/tools/exp_bridge_order_denominator_gate.py @@ -0,0 +1,206 @@ +#!/usr/bin/env python3 +""" +exp_bridge_order_denominator_gate.py + +Falsification attempt for ORDER_DENOMINATOR_GATE on bridge/perimeter sequences +already present in the D-ND lab context: + +- prime metric connection fluctuations from g=(p/2)^2 +- prime metric curvature fluctuations dR +- zeta trace-bridge nonlinear residuals +- hydrogen bound-level spacings from the QxE bridge + +The coherent endpoint is the observed/generated bridge order. The illusory +endpoint is a marginal-preserving permutation. Canonical observables come from +observables_registry.py. +""" + +from __future__ import annotations + +import argparse +import json +import math +from pathlib import Path + +import numpy as np + +from exp_semireal_order_denominator_gate import analyze_sequence, compact, normalize +from observables_registry import OBSERVABLES_REGISTRY_VERSION, OBSERVABLES_CANONICAL + + +OBS_NAMES = list(OBSERVABLES_CANONICAL.keys()) +PHI = (1.0 + math.sqrt(5.0)) / 2.0 +LAMBDA = -1.0 / PHI**2 +DATA_DIR = Path(__file__).parent / "data" + + +def sieve_primes_for_count(n_primes: int) -> np.ndarray: + if n_primes < 6: + limit = 20 + else: + limit = int(n_primes * (math.log(n_primes) + math.log(math.log(n_primes))) * 1.35) + while True: + sieve = np.ones(limit + 1, dtype=bool) + sieve[:2] = False + for p in range(2, int(limit**0.5) + 1): + if sieve[p]: + sieve[p * p : limit + 1 : p] = False + primes = np.flatnonzero(sieve) + if len(primes) >= n_primes: + return primes[:n_primes].astype(float) + limit *= 2 + + +def positive_bridge_values(values: np.ndarray) -> np.ndarray: + """Map a signed bridge observable to positive values without changing order.""" + values = np.asarray(values, dtype=float) + values = values[np.isfinite(values)] + values = np.abs(values) + return normalize(values + 1e-12) + + +def prime_metric_delta_gamma(n_values: int) -> np.ndarray: + primes = sieve_primes_for_count(n_values + 3) + p = primes.astype(float) + tau = np.log(p) + metric = (p / 2.0) ** 2 + dg = np.diff(metric) + dtau = np.diff(tau) + mid = (metric[:-1] + metric[1:]) / 2.0 + gamma = dg / (2.0 * mid * dtau) + delta_gamma = np.diff(gamma) + return positive_bridge_values(delta_gamma[:n_values]) + + +def prime_metric_dR(n_values: int) -> np.ndarray: + primes = sieve_primes_for_count(n_values + 3) + seq = primes.astype(float) + t = np.log(seq) + a = seq / 2.0 + dt = np.diff(t) + dt_mid = (dt[:-1] + dt[1:]) / 2.0 + da = np.diff(a) + a_prime = da / dt + da_prime = np.diff(a_prime) + a_double_prime = da_prime / dt_mid + r_n = 2.0 * a_double_prime / a[1:-1] + d_r = r_n - 2.0 + return positive_bridge_values(d_r[:n_values]) + + +def load_zeta_zeros(n_zeros: int) -> np.ndarray: + zeros_file = DATA_DIR / "odlyzko_cache" / "zeros1.txt" + if not zeros_file.exists(): + raise RuntimeError(f"{zeros_file} not found") + zeros: list[float] = [] + with zeros_file.open() as f: + for line in f: + line = line.strip() + if not line: + continue + zeros.append(float(line)) + if len(zeros) >= n_zeros: + break + if len(zeros) < n_zeros: + raise RuntimeError(f"only {len(zeros)} zeta zeros available, need {n_zeros}") + return np.array(zeros, dtype=float) + + +def dnd_map_trajectory(x0: float, n_iter: int) -> np.ndarray: + x = float(x0) + traj = [x] + for _ in range(n_iter): + if abs(x) < 1e-15: + break + x = 1.0 + 1.0 / x + if not np.isfinite(x): + break + traj.append(x) + return np.array(traj, dtype=float) + + +def zeta_trace_residual(n_values: int, step: int = 5) -> np.ndarray: + zeros = load_zeta_zeros(n_values) + residuals = [] + for x0 in zeros: + traj = dnd_map_trajectory(float(x0), max(step + 2, 15)) + if len(traj) <= step: + continue + linear = PHI + (float(x0) - PHI) * (LAMBDA**step) + residuals.append(traj[step] - linear) + return positive_bridge_values(np.array(residuals[:n_values], dtype=float)) + + +def hydrogen_bound_level_spacings(n_values: int) -> np.ndarray: + # Atomic units: E_n = -1/(2n^2). Positive adjacent spacings shrink smoothly. + n = np.arange(1, n_values + 2, dtype=float) + energy = -1.0 / (2.0 * n**2) + spacings = np.diff(energy) + return normalize(spacings) + + +def build_sequences(args: argparse.Namespace) -> dict[str, np.ndarray]: + return { + "prime_metric_delta_gamma_abs": prime_metric_delta_gamma(args.n_gaps), + "prime_metric_dR_abs": prime_metric_dR(args.n_gaps), + "zeta_trace_residual_step5_abs": zeta_trace_residual(args.zeta_values, step=5), + "hydrogen_bound_level_spacings": hydrogen_bound_level_spacings(args.n_gaps), + } + + +def run(args: argparse.Namespace) -> dict: + root_rng = np.random.default_rng(args.seed) + sequences = build_sequences(args) + perimeters = {} + for name, base in sequences.items(): + perimeters[name] = analyze_sequence(name, base, args, root_rng) + + output = { + "experiment": "bridge_order_denominator_gate", + "category": "gate_falsification_bridge", + "question": "Does ORDER_DENOMINATOR_GATE survive on D-ND bridge sequences beyond prime/zeta/logistic gaps?", + "observables_registry": OBSERVABLES_REGISTRY_VERSION, + "observables_used": OBS_NAMES, + "params": vars(args), + "matrix": compact(perimeters), + "perimeters": perimeters, + } + + out_path = Path(args.out) + out_path.parent.mkdir(parents=True, exist_ok=True) + with out_path.open("w") as f: + json.dump(output, f, indent=2) + + print(f"observables_registry={OBSERVABLES_REGISTRY_VERSION}") + print(f"observables_used={OBS_NAMES}") + print("perimeter n one_sided stable0 stable1 dist_gate ambiguous_gate") + for name, row in output["matrix"].items(): + print( + f"{name:>34s} " + f"{row['n_gaps']:>5d} " + f"{','.join(row['coherent_one_sided_observables']) or '[]':>22s} " + f"{row['stable_count_coherent']:>7.3f} " + f"{row['stable_count_illusory']:>7.3f} " + f"{row['endpoint_distance_one_sided_gated']:>9.3f} " + f"{row['ambiguous_beta_one_sided_gated']}" + ) + print(f"saved {out_path}") + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--n-gaps", type=int, default=4096) + parser.add_argument("--zeta-values", type=int, default=2000) + parser.add_argument("--n-replicates", type=int, default=20) + parser.add_argument("--n-beta", type=int, default=11) + parser.add_argument("--n-baseline", type=int, default=32) + parser.add_argument("--z-min", type=float, default=2.0) + parser.add_argument("--seed", type=int, default=202605070942) + parser.add_argument("--out", default="tools/data/bridge_order_denominator_gate_20260507_0942.json") + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() diff --git a/tools/data/seme.json b/tools/data/seme.json index 0c6e20e89b979c05b2f3d06089e734d017a0159d..a081e04934a85172c6941c0e2f636bbace866214 --- a/tools/data/seme.json +++ b/tools/data/seme.json @@ -38,12 +38,12 @@ { "tipo": "vincolo", "id": "ORDER_DENOMINATOR_GATE", - "claim": "Il denominator gate trasferisce come supporto one-sided dell'ordine quando l'ordine e visibile agli osservabili canonici del perimetro, non come endpoint-stable support a due poli. Nel perimetro sintetico agent_20260507_0901, 4/4 domini non-BOUNDARY hanno endpoint_stable_observables=[] e polo coerente stable_count 3.0-5.0. Nel perimetro semi-reale agent_20260507_0923, primi e zeta trasferiscono (primi: SR,L1,triple_var; zeta: SR,L2), ma logistic_return_intervals e blank: stable_count coerente 0.0-0.2. La beta 0.30 e coordinata del protocollo quando compare, non coordinata universale.", + "claim": "Il denominator gate trasferisce come supporto one-sided dell'ordine quando l'ordine e visibile agli osservabili canonici del perimetro, non come endpoint-stable support a due poli. Nel perimetro sintetico agent_20260507_0901, 4/4 domini non-BOUNDARY hanno endpoint_stable_observables=[] e polo coerente stable_count 3.0-5.0. Nel perimetro semi-reale agent_20260507_0923, primi e zeta trasferiscono (primi: SR,L1,triple_var; zeta: SR,L2), ma logistic_return_intervals e blank: stable_count coerente 0.0-0.2. Nel perimetro bridge agent_20260507_0942, prime_metric_delta_gamma_abs, prime_metric_dR_abs, zeta_trace_residual_step5_abs e hydrogen_bound_level_spacings trasferiscono su tutti i 5 osservabili canonici con endpoint_stable_observables=[]; e supporto perimetro-bridge, non universalita del gate. La beta 0.30/0.40/0.50 resta coordinata del protocollo quando compare, non coordinata universale.", "intensita": 0.92, "porta": "META", "manuale": true, "condensato_ref": "A4,A8,A14,C2", - "origine": "cycle agent_20260507_0901 + agent_20260507_0923: transfer matrix sintetica e falsificazione semi-reale su primi, zeta, logistic returns", + "origine": "cycle agent_20260507_0901 + agent_20260507_0923 + agent_20260507_0942: transfer matrix sintetica, falsificazione semi-reale su primi/zeta/logistic returns, e perimetri bridge metric/trace/QxE", "added_at": "2026-05-07T09:01:00+00:00" }, { @@ -333,4 +333,4 @@ "timestamp": "2026-04-04T19:34:08.014Z" } ] -} \ No newline at end of file +} diff --git a/tools/exp_bridge_order_denominator_gate.py b/tools/exp_bridge_order_denominator_gate.py new file mode 100644 index 0000000000000000000000000000000000000000..17286d6199454f089722ec1e0e6bad01f25f3b92 --- /dev/null +++ b/tools/exp_bridge_order_denominator_gate.py @@ -0,0 +1,206 @@ +#!/usr/bin/env python3 +""" +exp_bridge_order_denominator_gate.py + +Falsification attempt for ORDER_DENOMINATOR_GATE on bridge/perimeter sequences +already present in the D-ND lab context: + +- prime metric connection fluctuations from g=(p/2)^2 +- prime metric curvature fluctuations dR +- zeta trace-bridge nonlinear residuals +- hydrogen bound-level spacings from the QxE bridge + +The coherent endpoint is the observed/generated bridge order. The illusory +endpoint is a marginal-preserving permutation. Canonical observables come from +observables_registry.py. +""" + +from __future__ import annotations + +import argparse +import json +import math +from pathlib import Path + +import numpy as np + +from exp_semireal_order_denominator_gate import analyze_sequence, compact, normalize +from observables_registry import OBSERVABLES_REGISTRY_VERSION, OBSERVABLES_CANONICAL + + +OBS_NAMES = list(OBSERVABLES_CANONICAL.keys()) +PHI = (1.0 + math.sqrt(5.0)) / 2.0 +LAMBDA = -1.0 / PHI**2 +DATA_DIR = Path(__file__).parent / "data" + + +def sieve_primes_for_count(n_primes: int) -> np.ndarray: + if n_primes < 6: + limit = 20 + else: + limit = int(n_primes * (math.log(n_primes) + math.log(math.log(n_primes))) * 1.35) + while True: + sieve = np.ones(limit + 1, dtype=bool) + sieve[:2] = False + for p in range(2, int(limit**0.5) + 1): + if sieve[p]: + sieve[p * p : limit + 1 : p] = False + primes = np.flatnonzero(sieve) + if len(primes) >= n_primes: + return primes[:n_primes].astype(float) + limit *= 2 + + +def positive_bridge_values(values: np.ndarray) -> np.ndarray: + """Map a signed bridge observable to positive values without changing order.""" + values = np.asarray(values, dtype=float) + values = values[np.isfinite(values)] + values = np.abs(values) + return normalize(values + 1e-12) + + +def prime_metric_delta_gamma(n_values: int) -> np.ndarray: + primes = sieve_primes_for_count(n_values + 3) + p = primes.astype(float) + tau = np.log(p) + metric = (p / 2.0) ** 2 + dg = np.diff(metric) + dtau = np.diff(tau) + mid = (metric[:-1] + metric[1:]) / 2.0 + gamma = dg / (2.0 * mid * dtau) + delta_gamma = np.diff(gamma) + return positive_bridge_values(delta_gamma[:n_values]) + + +def prime_metric_dR(n_values: int) -> np.ndarray: + primes = sieve_primes_for_count(n_values + 3) + seq = primes.astype(float) + t = np.log(seq) + a = seq / 2.0 + dt = np.diff(t) + dt_mid = (dt[:-1] + dt[1:]) / 2.0 + da = np.diff(a) + a_prime = da / dt + da_prime = np.diff(a_prime) + a_double_prime = da_prime / dt_mid + r_n = 2.0 * a_double_prime / a[1:-1] + d_r = r_n - 2.0 + return positive_bridge_values(d_r[:n_values]) + + +def load_zeta_zeros(n_zeros: int) -> np.ndarray: + zeros_file = DATA_DIR / "odlyzko_cache" / "zeros1.txt" + if not zeros_file.exists(): + raise RuntimeError(f"{zeros_file} not found") + zeros: list[float] = [] + with zeros_file.open() as f: + for line in f: + line = line.strip() + if not line: + continue + zeros.append(float(line)) + if len(zeros) >= n_zeros: + break + if len(zeros) < n_zeros: + raise RuntimeError(f"only {len(zeros)} zeta zeros available, need {n_zeros}") + return np.array(zeros, dtype=float) + + +def dnd_map_trajectory(x0: float, n_iter: int) -> np.ndarray: + x = float(x0) + traj = [x] + for _ in range(n_iter): + if abs(x) < 1e-15: + break + x = 1.0 + 1.0 / x + if not np.isfinite(x): + break + traj.append(x) + return np.array(traj, dtype=float) + + +def zeta_trace_residual(n_values: int, step: int = 5) -> np.ndarray: + zeros = load_zeta_zeros(n_values) + residuals = [] + for x0 in zeros: + traj = dnd_map_trajectory(float(x0), max(step + 2, 15)) + if len(traj) <= step: + continue + linear = PHI + (float(x0) - PHI) * (LAMBDA**step) + residuals.append(traj[step] - linear) + return positive_bridge_values(np.array(residuals[:n_values], dtype=float)) + + +def hydrogen_bound_level_spacings(n_values: int) -> np.ndarray: + # Atomic units: E_n = -1/(2n^2). Positive adjacent spacings shrink smoothly. + n = np.arange(1, n_values + 2, dtype=float) + energy = -1.0 / (2.0 * n**2) + spacings = np.diff(energy) + return normalize(spacings) + + +def build_sequences(args: argparse.Namespace) -> dict[str, np.ndarray]: + return { + "prime_metric_delta_gamma_abs": prime_metric_delta_gamma(args.n_gaps), + "prime_metric_dR_abs": prime_metric_dR(args.n_gaps), + "zeta_trace_residual_step5_abs": zeta_trace_residual(args.zeta_values, step=5), + "hydrogen_bound_level_spacings": hydrogen_bound_level_spacings(args.n_gaps), + } + + +def run(args: argparse.Namespace) -> dict: + root_rng = np.random.default_rng(args.seed) + sequences = build_sequences(args) + perimeters = {} + for name, base in sequences.items(): + perimeters[name] = analyze_sequence(name, base, args, root_rng) + + output = { + "experiment": "bridge_order_denominator_gate", + "category": "gate_falsification_bridge", + "question": "Does ORDER_DENOMINATOR_GATE survive on D-ND bridge sequences beyond prime/zeta/logistic gaps?", + "observables_registry": OBSERVABLES_REGISTRY_VERSION, + "observables_used": OBS_NAMES, + "params": vars(args), + "matrix": compact(perimeters), + "perimeters": perimeters, + } + + out_path = Path(args.out) + out_path.parent.mkdir(parents=True, exist_ok=True) + with out_path.open("w") as f: + json.dump(output, f, indent=2) + + print(f"observables_registry={OBSERVABLES_REGISTRY_VERSION}") + print(f"observables_used={OBS_NAMES}") + print("perimeter n one_sided stable0 stable1 dist_gate ambiguous_gate") + for name, row in output["matrix"].items(): + print( + f"{name:>34s} " + f"{row['n_gaps']:>5d} " + f"{','.join(row['coherent_one_sided_observables']) or '[]':>22s} " + f"{row['stable_count_coherent']:>7.3f} " + f"{row['stable_count_illusory']:>7.3f} " + f"{row['endpoint_distance_one_sided_gated']:>9.3f} " + f"{row['ambiguous_beta_one_sided_gated']}" + ) + print(f"saved {out_path}") + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--n-gaps", type=int, default=4096) + parser.add_argument("--zeta-values", type=int, default=2000) + parser.add_argument("--n-replicates", type=int, default=20) + parser.add_argument("--n-beta", type=int, default=11) + parser.add_argument("--n-baseline", type=int, default=32) + parser.add_argument("--z-min", type=float, default=2.0) + parser.add_argument("--seed", type=int, default=202605070942) + parser.add_argument("--out", default="tools/data/bridge_order_denominator_gate_20260507_0942.json") + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() codex Il seme ora contiene il nuovo perimetro, con la limitazione esplicita: bridge-support, non universalità. Scrivo il report finale e poi verifico che i file JSON e Markdown siano presenti. apply patch patch: completed /opt/MM_D-ND/tools/data/reports/agent_20260507_0942.md diff --git a/tools/data/reports/agent_20260507_0942.md b/tools/data/reports/agent_20260507_0942.md new file mode 100644 index 0000000000000000000000000000000000000000..da245bdaa717b348e2ecdd515e30dcb01065e2b4 --- /dev/null +++ b/tools/data/reports/agent_20260507_0942.md @@ -0,0 +1,131 @@ +# Agent Report — Bridge Order Denominator Gate + +timestamp: 2026-05-07 09:42 UTC +category: gate_falsification_bridge +verdict: scoped_operator +observables_registry: 1.0.0-2026-05-06 +observables_used: [SR, SR2, L1, L2, triple_var] +tool: tools/exp_bridge_order_denominator_gate.py +data: tools/data/bridge_order_denominator_gate_20260507_0942.json +seed_check: tools/data/bridge_order_denominator_gate_20260507_0942_seedcheck.json + +## Claim Under Test + +Verificato dal campo vivo: `ORDER_DENOMINATOR_GATE` e gia stato testato su +perimetri sintetici e semi-reali. Il mandato corrente chiede domini fisici o +ponte gia presenti nel sistema, con shuffle marginal-preserving. + +Domanda: se il perimetro e una sequenza bridge D-ND, il supporto denominatore +resta one-sided o compare un controesempio? + +Perimetri: + +- `prime_metric_delta_gamma_abs`: fluttuazioni della connessione metrica da + `g=(p/2)^2`. +- `prime_metric_dR_abs`: fluttuazioni di curvatura discreta `dR=R-2` dalla + stessa metrica. +- `zeta_trace_residual_step5_abs`: residuo non-lineare del trace bridge sugli + zeri di zeta al passo 5. +- `hydrogen_bound_level_spacings`: spacing dei livelli legati + `E_n=-1/(2n^2)`, ponte QxE. + +Gate: osservabile stabile se `abs(original - shuffle_mean) / shuffle_std >= 2`. + +## Deposito Numerico + +Run principale: `n_replicates=20`, `n_beta=11`, `n_baseline=32`, +`seed=202605070942`. Seed check: `n_replicates=12`, `n_baseline=24`, +`seed=202605070943`. + +| perimeter | coherent one-sided observables | stable_count coherent | stable_count illusory | endpoint distance gated | beta ambiguous gated | +|---|---:|---:|---:|---:|---:| +| prime_metric_delta_gamma_abs | SR, SR2, L1, L2, triple_var | 5.000 | 0.250 | 4.415 | [] | +| prime_metric_dR_abs | SR, SR2, L1, L2, triple_var | 5.000 | 0.350 | 4.414 | 0.30 | +| zeta_trace_residual_step5_abs | SR, SR2, L1, L2, triple_var | 5.000 | 0.050 | 4.415 | 0.30 | +| hydrogen_bound_level_spacings | SR, SR2, L1, L2, triple_var | 5.000 | 0.200 | 4.416 | 0.40 | + +Seed check: + +| perimeter | coherent one-sided observables | stable_count coherent | stable_count illusory | endpoint distance gated | beta ambiguous gated | +|---|---:|---:|---:|---:|---:| +| prime_metric_delta_gamma_abs | SR, SR2, L1, L2, triple_var | 5.000 | 0.167 | 4.377 | [] | +| prime_metric_dR_abs | SR, SR2, L1, L2, triple_var | 5.000 | 0.083 | 4.376 | [] | +| zeta_trace_residual_step5_abs | SR, SR2, L1, L2, triple_var | 5.000 | 0.083 | 4.377 | 0.30 | +| hydrogen_bound_level_spacings | SR, SR2, L1, L2, triple_var | 5.000 | 0.250 | 4.378 | 0.40, 0.50 | + +Endpoint-stable observables: `[]` in all four perimeters in both runs. + +## Risultato + +1. **The bridge perimeter carries full canonical one-sided support.** + + Each bridge sequence transfers on all five canonical observables. The + coherent endpoint has stable count `5.000` in both runs; the illusory endpoint + stays weak-denominator under the same marginal. + +2. **The both-endpoint support remains blank.** + + `endpoint_stable_observables=[]` repeats across metric, trace, and QxE + bridge perimeters. The gate is not two endpoint classes with stable + denominators. It is support on the side where order survives its shuffle null. + +3. **The logistic counter-scope from 09:23 still matters.** + + This result extends the gate into bridge sequences; it does not erase the + logistic return blank. The transferable object is order visible to the + canonical gap-observable contract. Return-time tail order still requires its + own observable perimetro. + +4. **The beta coordinate does not universalize.** + + Metric `delta_gamma` has no ambiguous gated beta in either run. Metric `dR` + has `0.30` in the main run and blank in seed check. Zeta trace keeps `0.30`. + Hydrogen gives `0.40` in the main run and `0.40, 0.50` in seed check. Beta is + protocol coordinate, not domain coordinate. + +## Consecutio + +`ORDER_DENOMINATOR_GATE` survives the bridge falsification: + +> In bridge perimeters whose order is carried by metric connection, +> curvature fluctuation, trace residual, or QxE level spacing sequences, the +> denominator gate transfers as full canonical one-sided support against a +> marginal-preserving shuffle. The logistic return perimeter remains the +> counter-scope for this observable contract. + +Next experiment: test the logistic counter-scope at the node regressivo: +symbolic itinerary block entropy, return-tail exponent, and recurrence-plot +diagonal statistics under the same original-vs-shuffle denominator gate. Do not +tune `z_min`. + +## Self-Audit: 5 Lenti + +L1 hard constraint vs bias: `endpoint_stable_observables=[]` is exact for the +declared perimeters and runs. The report does not claim universal absence of +illusory support; illusory stable counts are `0.050-0.350` in the main run. + +L2 quantity vs ratio: stable counts, endpoint distances, and z means are +reported before interpretation. No percent drift claim is used. + +L3 no silent patching: 09:23 found a logistic blank. This report does not patch +that blank; it adds bridge perimeters and keeps the counter-scope. + +L4 edge cases: hydrogen beta changes from `0.40` to `0.40,0.50` in seed check, +and metric `dR` beta drops from `0.30` to blank. The beta claim is explicitly +non-universal. + +L5 re-discovery vs discovery: metric, zeta, and hydrogen sequences sit near +known RMT/arithmetical/spectral order diagnostics. This report claims operator +scope inside the lab gate protocol, not a new theorem. + +## Fonti + +- Verificato: `tools/data/agent_field_live.md` +- Verificato: `tools/LAB_AGENT_CONTEXT.md` +- Verificato: `tools/observables_registry.py` +- Verificato: `tools/exp_bridge_order_denominator_gate.py` +- Verificato: `tools/data/bridge_order_denominator_gate_20260507_0942.json` +- Verificato: `tools/data/bridge_order_denominator_gate_20260507_0942_seedcheck.json` +- Inferito: bridge perimeters carry full canonical one-sided support because + all five observables pass the original-vs-shuffle gate at the coherent + endpoint and fail endpoint-stable support in both runs. diff --git a/tools/data/seme.json b/tools/data/seme.json index 0c6e20e89b979c05b2f3d06089e734d017a0159d..a081e04934a85172c6941c0e2f636bbace866214 --- a/tools/data/seme.json +++ b/tools/data/seme.json @@ -38,12 +38,12 @@ { "tipo": "vincolo", "id": "ORDER_DENOMINATOR_GATE", - "claim": "Il denominator gate trasferisce come supporto one-sided dell'ordine quando l'ordine e visibile agli osservabili canonici del perimetro, non come endpoint-stable support a due poli. Nel perimetro sintetico agent_20260507_0901, 4/4 domini non-BOUNDARY hanno endpoint_stable_observables=[] e polo coerente stable_count 3.0-5.0. Nel perimetro semi-reale agent_20260507_0923, primi e zeta trasferiscono (primi: SR,L1,triple_var; zeta: SR,L2), ma logistic_return_intervals e blank: stable_count coerente 0.0-0.2. La beta 0.30 e coordinata del protocollo quando compare, non coordinata universale.", + "claim": "Il denominator gate trasferisce come supporto one-sided dell'ordine quando l'ordine e visibile agli osservabili canonici del perimetro, non come endpoint-stable support a due poli. Nel perimetro sintetico agent_20260507_0901, 4/4 domini non-BOUNDARY hanno endpoint_stable_observables=[] e polo coerente stable_count 3.0-5.0. Nel perimetro semi-reale agent_20260507_0923, primi e zeta trasferiscono (primi: SR,L1,triple_var; zeta: SR,L2), ma logistic_return_intervals e blank: stable_count coerente 0.0-0.2. Nel perimetro bridge agent_20260507_0942, prime_metric_delta_gamma_abs, prime_metric_dR_abs, zeta_trace_residual_step5_abs e hydrogen_bound_level_spacings trasferiscono su tutti i 5 osservabili canonici con endpoint_stable_observables=[]; e supporto perimetro-bridge, non universalita del gate. La beta 0.30/0.40/0.50 resta coordinata del protocollo quando compare, non coordinata universale.", "intensita": 0.92, "porta": "META", "manuale": true, "condensato_ref": "A4,A8,A14,C2", - "origine": "cycle agent_20260507_0901 + agent_20260507_0923: transfer matrix sintetica e falsificazione semi-reale su primi, zeta, logistic returns", + "origine": "cycle agent_20260507_0901 + agent_20260507_0923 + agent_20260507_0942: transfer matrix sintetica, falsificazione semi-reale su primi/zeta/logistic returns, e perimetri bridge metric/trace/QxE", "added_at": "2026-05-07T09:01:00+00:00" }, { @@ -333,4 +333,4 @@ "timestamp": "2026-04-04T19:34:08.014Z" } ] -} \ No newline at end of file +} diff --git a/tools/exp_bridge_order_denominator_gate.py b/tools/exp_bridge_order_denominator_gate.py new file mode 100644 index 0000000000000000000000000000000000000000..17286d6199454f089722ec1e0e6bad01f25f3b92 --- /dev/null +++ b/tools/exp_bridge_order_denominator_gate.py @@ -0,0 +1,206 @@ +#!/usr/bin/env python3 +""" +exp_bridge_order_denominator_gate.py + +Falsification attempt for ORDER_DENOMINATOR_GATE on bridge/perimeter sequences +already present in the D-ND lab context: + +- prime metric connection fluctuations from g=(p/2)^2 +- prime metric curvature fluctuations dR +- zeta trace-bridge nonlinear residuals +- hydrogen bound-level spacings from the QxE bridge + +The coherent endpoint is the observed/generated bridge order. The illusory +endpoint is a marginal-preserving permutation. Canonical observables come from +observables_registry.py. +""" + +from __future__ import annotations + +import argparse +import json +import math +from pathlib import Path + +import numpy as np + +from exp_semireal_order_denominator_gate import analyze_sequence, compact, normalize +from observables_registry import OBSERVABLES_REGISTRY_VERSION, OBSERVABLES_CANONICAL + + +OBS_NAMES = list(OBSERVABLES_CANONICAL.keys()) +PHI = (1.0 + math.sqrt(5.0)) / 2.0 +LAMBDA = -1.0 / PHI**2 +DATA_DIR = Path(__file__).parent / "data" + + +def sieve_primes_for_count(n_primes: int) -> np.ndarray: + if n_primes < 6: + limit = 20 + else: + limit = int(n_primes * (math.log(n_primes) + math.log(math.log(n_primes))) * 1.35) + while True: + sieve = np.ones(limit + 1, dtype=bool) + sieve[:2] = False + for p in range(2, int(limit**0.5) + 1): + if sieve[p]: + sieve[p * p : limit + 1 : p] = False + primes = np.flatnonzero(sieve) + if len(primes) >= n_primes: + return primes[:n_primes].astype(float) + limit *= 2 + + +def positive_bridge_values(values: np.ndarray) -> np.ndarray: + """Map a signed bridge observable to positive values without changing order.""" + values = np.asarray(values, dtype=float) + values = values[np.isfinite(values)] + values = np.abs(values) + return normalize(values + 1e-12) + + +def prime_metric_delta_gamma(n_values: int) -> np.ndarray: + primes = sieve_primes_for_count(n_values + 3) + p = primes.astype(float) + tau = np.log(p) + metric = (p / 2.0) ** 2 + dg = np.diff(metric) + dtau = np.diff(tau) + mid = (metric[:-1] + metric[1:]) / 2.0 + gamma = dg / (2.0 * mid * dtau) + delta_gamma = np.diff(gamma) + return positive_bridge_values(delta_gamma[:n_values]) + + +def prime_metric_dR(n_values: int) -> np.ndarray: + primes = sieve_primes_for_count(n_values + 3) + seq = primes.astype(float) + t = np.log(seq) + a = seq / 2.0 + dt = np.diff(t) + dt_mid = (dt[:-1] + dt[1:]) / 2.0 + da = np.diff(a) + a_prime = da / dt + da_prime = np.diff(a_prime) + a_double_prime = da_prime / dt_mid + r_n = 2.0 * a_double_prime / a[1:-1] + d_r = r_n - 2.0 + return positive_bridge_values(d_r[:n_values]) + + +def load_zeta_zeros(n_zeros: int) -> np.ndarray: + zeros_file = DATA_DIR / "odlyzko_cache" / "zeros1.txt" + if not zeros_file.exists(): + raise RuntimeError(f"{zeros_file} not found") + zeros: list[float] = [] + with zeros_file.open() as f: + for line in f: + line = line.strip() + if not line: + continue + zeros.append(float(line)) + if len(zeros) >= n_zeros: + break + if len(zeros) < n_zeros: + raise RuntimeError(f"only {len(zeros)} zeta zeros available, need {n_zeros}") + return np.array(zeros, dtype=float) + + +def dnd_map_trajectory(x0: float, n_iter: int) -> np.ndarray: + x = float(x0) + traj = [x] + for _ in range(n_iter): + if abs(x) < 1e-15: + break + x = 1.0 + 1.0 / x + if not np.isfinite(x): + break + traj.append(x) + return np.array(traj, dtype=float) + + +def zeta_trace_residual(n_values: int, step: int = 5) -> np.ndarray: + zeros = load_zeta_zeros(n_values) + residuals = [] + for x0 in zeros: + traj = dnd_map_trajectory(float(x0), max(step + 2, 15)) + if len(traj) <= step: + continue + linear = PHI + (float(x0) - PHI) * (LAMBDA**step) + residuals.append(traj[step] - linear) + return positive_bridge_values(np.array(residuals[:n_values], dtype=float)) + + +def hydrogen_bound_level_spacings(n_values: int) -> np.ndarray: + # Atomic units: E_n = -1/(2n^2). Positive adjacent spacings shrink smoothly. + n = np.arange(1, n_values + 2, dtype=float) + energy = -1.0 / (2.0 * n**2) + spacings = np.diff(energy) + return normalize(spacings) + + +def build_sequences(args: argparse.Namespace) -> dict[str, np.ndarray]: + return { + "prime_metric_delta_gamma_abs": prime_metric_delta_gamma(args.n_gaps), + "prime_metric_dR_abs": prime_metric_dR(args.n_gaps), + "zeta_trace_residual_step5_abs": zeta_trace_residual(args.zeta_values, step=5), + "hydrogen_bound_level_spacings": hydrogen_bound_level_spacings(args.n_gaps), + } + + +def run(args: argparse.Namespace) -> dict: + root_rng = np.random.default_rng(args.seed) + sequences = build_sequences(args) + perimeters = {} + for name, base in sequences.items(): + perimeters[name] = analyze_sequence(name, base, args, root_rng) + + output = { + "experiment": "bridge_order_denominator_gate", + "category": "gate_falsification_bridge", + "question": "Does ORDER_DENOMINATOR_GATE survive on D-ND bridge sequences beyond prime/zeta/logistic gaps?", + "observables_registry": OBSERVABLES_REGISTRY_VERSION, + "observables_used": OBS_NAMES, + "params": vars(args), + "matrix": compact(perimeters), + "perimeters": perimeters, + } + + out_path = Path(args.out) + out_path.parent.mkdir(parents=True, exist_ok=True) + with out_path.open("w") as f: + json.dump(output, f, indent=2) + + print(f"observables_registry={OBSERVABLES_REGISTRY_VERSION}") + print(f"observables_used={OBS_NAMES}") + print("perimeter n one_sided stable0 stable1 dist_gate ambiguous_gate") + for name, row in output["matrix"].items(): + print( + f"{name:>34s} " + f"{row['n_gaps']:>5d} " + f"{','.join(row['coherent_one_sided_observables']) or '[]':>22s} " + f"{row['stable_count_coherent']:>7.3f} " + f"{row['stable_count_illusory']:>7.3f} " + f"{row['endpoint_distance_one_sided_gated']:>9.3f} " + f"{row['ambiguous_beta_one_sided_gated']}" + ) + print(f"saved {out_path}") + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--n-gaps", type=int, default=4096) + parser.add_argument("--zeta-values", type=int, default=2000) + parser.add_argument("--n-replicates", type=int, default=20) + parser.add_argument("--n-beta", type=int, default=11) + parser.add_argument("--n-baseline", type=int, default=32) + parser.add_argument("--z-min", type=float, default=2.0) + parser.add_argument("--seed", type=int, default=202605070942) + parser.add_argument("--out", default="tools/data/bridge_order_denominator_gate_20260507_0942.json") + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() diff --git a/tools/data/reports/agent_20260507_0942.md b/tools/data/reports/agent_20260507_0942.md new file mode 100644 index 0000000000000000000000000000000000000000..da245bdaa717b348e2ecdd515e30dcb01065e2b4 --- /dev/null +++ b/tools/data/reports/agent_20260507_0942.md @@ -0,0 +1,131 @@ +# Agent Report — Bridge Order Denominator Gate + +timestamp: 2026-05-07 09:42 UTC +category: gate_falsification_bridge +verdict: scoped_operator +observables_registry: 1.0.0-2026-05-06 +observables_used: [SR, SR2, L1, L2, triple_var] +tool: tools/exp_bridge_order_denominator_gate.py +data: tools/data/bridge_order_denominator_gate_20260507_0942.json +seed_check: tools/data/bridge_order_denominator_gate_20260507_0942_seedcheck.json + +## Claim Under Test + +Verificato dal campo vivo: `ORDER_DENOMINATOR_GATE` e gia stato testato su +perimetri sintetici e semi-reali. Il mandato corrente chiede domini fisici o +ponte gia presenti nel sistema, con shuffle marginal-preserving. + +Domanda: se il perimetro e una sequenza bridge D-ND, il supporto denominatore +resta one-sided o compare un controesempio? + +Perimetri: + +- `prime_metric_delta_gamma_abs`: fluttuazioni della connessione metrica da + `g=(p/2)^2`. +- `prime_metric_dR_abs`: fluttuazioni di curvatura discreta `dR=R-2` dalla + stessa metrica. +- `zeta_trace_residual_step5_abs`: residuo non-lineare del trace bridge sugli + zeri di zeta al passo 5. +- `hydrogen_bound_level_spacings`: spacing dei livelli legati + `E_n=-1/(2n^2)`, ponte QxE. + +Gate: osservabile stabile se `abs(original - shuffle_mean) / shuffle_std >= 2`. + +## Deposito Numerico + +Run principale: `n_replicates=20`, `n_beta=11`, `n_baseline=32`, +`seed=202605070942`. Seed check: `n_replicates=12`, `n_baseline=24`, +`seed=202605070943`. + +| perimeter | coherent one-sided observables | stable_count coherent | stable_count illusory | endpoint distance gated | beta ambiguous gated | +|---|---:|---:|---:|---:|---:| +| prime_metric_delta_gamma_abs | SR, SR2, L1, L2, triple_var | 5.000 | 0.250 | 4.415 | [] | +| prime_metric_dR_abs | SR, SR2, L1, L2, triple_var | 5.000 | 0.350 | 4.414 | 0.30 | +| zeta_trace_residual_step5_abs | SR, SR2, L1, L2, triple_var | 5.000 | 0.050 | 4.415 | 0.30 | +| hydrogen_bound_level_spacings | SR, SR2, L1, L2, triple_var | 5.000 | 0.200 | 4.416 | 0.40 | + +Seed check: + +| perimeter | coherent one-sided observables | stable_count coherent | stable_count illusory | endpoint distance gated | beta ambiguous gated | +|---|---:|---:|---:|---:|---:| +| prime_metric_delta_gamma_abs | SR, SR2, L1, L2, triple_var | 5.000 | 0.167 | 4.377 | [] | +| prime_metric_dR_abs | SR, SR2, L1, L2, triple_var | 5.000 | 0.083 | 4.376 | [] | +| zeta_trace_residual_step5_abs | SR, SR2, L1, L2, triple_var | 5.000 | 0.083 | 4.377 | 0.30 | +| hydrogen_bound_level_spacings | SR, SR2, L1, L2, triple_var | 5.000 | 0.250 | 4.378 | 0.40, 0.50 | + +Endpoint-stable observables: `[]` in all four perimeters in both runs. + +## Risultato + +1. **The bridge perimeter carries full canonical one-sided support.** + + Each bridge sequence transfers on all five canonical observables. The + coherent endpoint has stable count `5.000` in both runs; the illusory endpoint + stays weak-denominator under the same marginal. + +2. **The both-endpoint support remains blank.** + + `endpoint_stable_observables=[]` repeats across metric, trace, and QxE + bridge perimeters. The gate is not two endpoint classes with stable + denominators. It is support on the side where order survives its shuffle null. + +3. **The logistic counter-scope from 09:23 still matters.** + + This result extends the gate into bridge sequences; it does not erase the + logistic return blank. The transferable object is order visible to the + canonical gap-observable contract. Return-time tail order still requires its + own observable perimetro. + +4. **The beta coordinate does not universalize.** + + Metric `delta_gamma` has no ambiguous gated beta in either run. Metric `dR` + has `0.30` in the main run and blank in seed check. Zeta trace keeps `0.30`. + Hydrogen gives `0.40` in the main run and `0.40, 0.50` in seed check. Beta is + protocol coordinate, not domain coordinate. + +## Consecutio + +`ORDER_DENOMINATOR_GATE` survives the bridge falsification: + +> In bridge perimeters whose order is carried by metric connection, +> curvature fluctuation, trace residual, or QxE level spacing sequences, the +> denominator gate transfers as full canonical one-sided support against a +> marginal-preserving shuffle. The logistic return perimeter remains the +> counter-scope for this observable contract. + +Next experiment: test the logistic counter-scope at the node regressivo: +symbolic itinerary block entropy, return-tail exponent, and recurrence-plot +diagonal statistics under the same original-vs-shuffle denominator gate. Do not +tune `z_min`. + +## Self-Audit: 5 Lenti + +L1 hard constraint vs bias: `endpoint_stable_observables=[]` is exact for the +declared perimeters and runs. The report does not claim universal absence of +illusory support; illusory stable counts are `0.050-0.350` in the main run. + +L2 quantity vs ratio: stable counts, endpoint distances, and z means are +reported before interpretation. No percent drift claim is used. + +L3 no silent patching: 09:23 found a logistic blank. This report does not patch +that blank; it adds bridge perimeters and keeps the counter-scope. + +L4 edge cases: hydrogen beta changes from `0.40` to `0.40,0.50` in seed check, +and metric `dR` beta drops from `0.30` to blank. The beta claim is explicitly +non-universal. + +L5 re-discovery vs discovery: metric, zeta, and hydrogen sequences sit near +known RMT/arithmetical/spectral order diagnostics. This report claims operator +scope inside the lab gate protocol, not a new theorem. + +## Fonti + +- Verificato: `tools/data/agent_field_live.md` +- Verificato: `tools/LAB_AGENT_CONTEXT.md` +- Verificato: `tools/observables_registry.py` +- Verificato: `tools/exp_bridge_order_denominator_gate.py` +- Verificato: `tools/data/bridge_order_denominator_gate_20260507_0942.json` +- Verificato: `tools/data/bridge_order_denominator_gate_20260507_0942_seedcheck.json` +- Inferito: bridge perimeters carry full canonical one-sided support because + all five observables pass the original-vs-shuffle gate at the coherent + endpoint and fail endpoint-stable support in both runs. diff --git a/tools/data/seme.json b/tools/data/seme.json index 0c6e20e89b979c05b2f3d06089e734d017a0159d..a081e04934a85172c6941c0e2f636bbace866214 --- a/tools/data/seme.json +++ b/tools/data/seme.json @@ -38,12 +38,12 @@ { "tipo": "vincolo", "id": "ORDER_DENOMINATOR_GATE", - "claim": "Il denominator gate trasferisce come supporto one-sided dell'ordine quando l'ordine e visibile agli osservabili canonici del perimetro, non come endpoint-stable support a due poli. Nel perimetro sintetico agent_20260507_0901, 4/4 domini non-BOUNDARY hanno endpoint_stable_observables=[] e polo coerente stable_count 3.0-5.0. Nel perimetro semi-reale agent_20260507_0923, primi e zeta trasferiscono (primi: SR,L1,triple_var; zeta: SR,L2), ma logistic_return_intervals e blank: stable_count coerente 0.0-0.2. La beta 0.30 e coordinata del protocollo quando compare, non coordinata universale.", + "claim": "Il denominator gate trasferisce come supporto one-sided dell'ordine quando l'ordine e visibile agli osservabili canonici del perimetro, non come endpoint-stable support a due poli. Nel perimetro sintetico agent_20260507_0901, 4/4 domini non-BOUNDARY hanno endpoint_stable_observables=[] e polo coerente stable_count 3.0-5.0. Nel perimetro semi-reale agent_20260507_0923, primi e zeta trasferiscono (primi: SR,L1,triple_var; zeta: SR,L2), ma logistic_return_intervals e blank: stable_count coerente 0.0-0.2. Nel perimetro bridge agent_20260507_0942, prime_metric_delta_gamma_abs, prime_metric_dR_abs, zeta_trace_residual_step5_abs e hydrogen_bound_level_spacings trasferiscono su tutti i 5 osservabili canonici con endpoint_stable_observables=[]; e supporto perimetro-bridge, non universalita del gate. La beta 0.30/0.40/0.50 resta coordinata del protocollo quando compare, non coordinata universale.", "intensita": 0.92, "porta": "META", "manuale": true, "condensato_ref": "A4,A8,A14,C2", - "origine": "cycle agent_20260507_0901 + agent_20260507_0923: transfer matrix sintetica e falsificazione semi-reale su primi, zeta, logistic returns", + "origine": "cycle agent_20260507_0901 + agent_20260507_0923 + agent_20260507_0942: transfer matrix sintetica, falsificazione semi-reale su primi/zeta/logistic returns, e perimetri bridge metric/trace/QxE", "added_at": "2026-05-07T09:01:00+00:00" }, { @@ -333,4 +333,4 @@ "timestamp": "2026-04-04T19:34:08.014Z" } ] -} \ No newline at end of file +} diff --git a/tools/exp_bridge_order_denominator_gate.py b/tools/exp_bridge_order_denominator_gate.py new file mode 100644 index 0000000000000000000000000000000000000000..17286d6199454f089722ec1e0e6bad01f25f3b92 --- /dev/null +++ b/tools/exp_bridge_order_denominator_gate.py @@ -0,0 +1,206 @@ +#!/usr/bin/env python3 +""" +exp_bridge_order_denominator_gate.py + +Falsification attempt for ORDER_DENOMINATOR_GATE on bridge/perimeter sequences +already present in the D-ND lab context: + +- prime metric connection fluctuations from g=(p/2)^2 +- prime metric curvature fluctuations dR +- zeta trace-bridge nonlinear residuals +- hydrogen bound-level spacings from the QxE bridge + +The coherent endpoint is the observed/generated bridge order. The illusory +endpoint is a marginal-preserving permutation. Canonical observables come from +observables_registry.py. +""" + +from __future__ import annotations + +import argparse +import json +import math +from pathlib import Path + +import numpy as np + +from exp_semireal_order_denominator_gate import analyze_sequence, compact, normalize +from observables_registry import OBSERVABLES_REGISTRY_VERSION, OBSERVABLES_CANONICAL + + +OBS_NAMES = list(OBSERVABLES_CANONICAL.keys()) +PHI = (1.0 + math.sqrt(5.0)) / 2.0 +LAMBDA = -1.0 / PHI**2 +DATA_DIR = Path(__file__).parent / "data" + + +def sieve_primes_for_count(n_primes: int) -> np.ndarray: + if n_primes < 6: + limit = 20 + else: + limit = int(n_primes * (math.log(n_primes) + math.log(math.log(n_primes))) * 1.35) + while True: + sieve = np.ones(limit + 1, dtype=bool) + sieve[:2] = False + for p in range(2, int(limit**0.5) + 1): + if sieve[p]: + sieve[p * p : limit + 1 : p] = False + primes = np.flatnonzero(sieve) + if len(primes) >= n_primes: + return primes[:n_primes].astype(float) + limit *= 2 + + +def positive_bridge_values(values: np.ndarray) -> np.ndarray: + """Map a signed bridge observable to positive values without changing order.""" + values = np.asarray(values, dtype=float) + values = values[np.isfinite(values)] + values = np.abs(values) + return normalize(values + 1e-12) + + +def prime_metric_delta_gamma(n_values: int) -> np.ndarray: + primes = sieve_primes_for_count(n_values + 3) + p = primes.astype(float) + tau = np.log(p) + metric = (p / 2.0) ** 2 + dg = np.diff(metric) + dtau = np.diff(tau) + mid = (metric[:-1] + metric[1:]) / 2.0 + gamma = dg / (2.0 * mid * dtau) + delta_gamma = np.diff(gamma) + return positive_bridge_values(delta_gamma[:n_values]) + + +def prime_metric_dR(n_values: int) -> np.ndarray: + primes = sieve_primes_for_count(n_values + 3) + seq = primes.astype(float) + t = np.log(seq) + a = seq / 2.0 + dt = np.diff(t) + dt_mid = (dt[:-1] + dt[1:]) / 2.0 + da = np.diff(a) + a_prime = da / dt + da_prime = np.diff(a_prime) + a_double_prime = da_prime / dt_mid + r_n = 2.0 * a_double_prime / a[1:-1] + d_r = r_n - 2.0 + return positive_bridge_values(d_r[:n_values]) + + +def load_zeta_zeros(n_zeros: int) -> np.ndarray: + zeros_file = DATA_DIR / "odlyzko_cache" / "zeros1.txt" + if not zeros_file.exists(): + raise RuntimeError(f"{zeros_file} not found") + zeros: list[float] = [] + with zeros_file.open() as f: + for line in f: + line = line.strip() + if not line: + continue + zeros.append(float(line)) + if len(zeros) >= n_zeros: + break + if len(zeros) < n_zeros: + raise RuntimeError(f"only {len(zeros)} zeta zeros available, need {n_zeros}") + return np.array(zeros, dtype=float) + + +def dnd_map_trajectory(x0: float, n_iter: int) -> np.ndarray: + x = float(x0) + traj = [x] + for _ in range(n_iter): + if abs(x) < 1e-15: + break + x = 1.0 + 1.0 / x + if not np.isfinite(x): + break + traj.append(x) + return np.array(traj, dtype=float) + + +def zeta_trace_residual(n_values: int, step: int = 5) -> np.ndarray: + zeros = load_zeta_zeros(n_values) + residuals = [] + for x0 in zeros: + traj = dnd_map_trajectory(float(x0), max(step + 2, 15)) + if len(traj) <= step: + continue + linear = PHI + (float(x0) - PHI) * (LAMBDA**step) + residuals.append(traj[step] - linear) + return positive_bridge_values(np.array(residuals[:n_values], dtype=float)) + + +def hydrogen_bound_level_spacings(n_values: int) -> np.ndarray: + # Atomic units: E_n = -1/(2n^2). Positive adjacent spacings shrink smoothly. + n = np.arange(1, n_values + 2, dtype=float) + energy = -1.0 / (2.0 * n**2) + spacings = np.diff(energy) + return normalize(spacings) + + +def build_sequences(args: argparse.Namespace) -> dict[str, np.ndarray]: + return { + "prime_metric_delta_gamma_abs": prime_metric_delta_gamma(args.n_gaps), + "prime_metric_dR_abs": prime_metric_dR(args.n_gaps), + "zeta_trace_residual_step5_abs": zeta_trace_residual(args.zeta_values, step=5), + "hydrogen_bound_level_spacings": hydrogen_bound_level_spacings(args.n_gaps), + } + + +def run(args: argparse.Namespace) -> dict: + root_rng = np.random.default_rng(args.seed) + sequences = build_sequences(args) + perimeters = {} + for name, base in sequences.items(): + perimeters[name] = analyze_sequence(name, base, args, root_rng) + + output = { + "experiment": "bridge_order_denominator_gate", + "category": "gate_falsification_bridge", + "question": "Does ORDER_DENOMINATOR_GATE survive on D-ND bridge sequences beyond prime/zeta/logistic gaps?", + "observables_registry": OBSERVABLES_REGISTRY_VERSION, + "observables_used": OBS_NAMES, + "params": vars(args), + "matrix": compact(perimeters), + "perimeters": perimeters, + } + + out_path = Path(args.out) + out_path.parent.mkdir(parents=True, exist_ok=True) + with out_path.open("w") as f: + json.dump(output, f, indent=2) + + print(f"observables_registry={OBSERVABLES_REGISTRY_VERSION}") + print(f"observables_used={OBS_NAMES}") + print("perimeter n one_sided stable0 stable1 dist_gate ambiguous_gate") + for name, row in output["matrix"].items(): + print( + f"{name:>34s} " + f"{row['n_gaps']:>5d} " + f"{','.join(row['coherent_one_sided_observables']) or '[]':>22s} " + f"{row['stable_count_coherent']:>7.3f} " + f"{row['stable_count_illusory']:>7.3f} " + f"{row['endpoint_distance_one_sided_gated']:>9.3f} " + f"{row['ambiguous_beta_one_sided_gated']}" + ) + print(f"saved {out_path}") + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--n-gaps", type=int, default=4096) + parser.add_argument("--zeta-values", type=int, default=2000) + parser.add_argument("--n-replicates", type=int, default=20) + parser.add_argument("--n-beta", type=int, default=11) + parser.add_argument("--n-baseline", type=int, default=32) + parser.add_argument("--z-min", type=float, default=2.0) + parser.add_argument("--seed", type=int, default=202605070942) + parser.add_argument("--out", default="tools/data/bridge_order_denominator_gate_20260507_0942.json") + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() exec /bin/bash -lc 'test -s tools/data/reports/agent_20260507_0942.md && wc -l tools/data/reports/agent_20260507_0942.md' in /opt/MM_D-ND succeeded in 0ms: 131 tools/data/reports/agent_20260507_0942.md exec /bin/bash -lc "rg -n 'Bridge Order|verdict|endpoint_stable|Consecutio|ORDER_DENOMINATOR_GATE' tools/data/reports/agent_20260507_0942.md tools/data/seme.json" in /opt/MM_D-ND succeeded in 0ms: tools/data/seme.json:40: "id": "ORDER_DENOMINATOR_GATE", tools/data/seme.json:41: "claim": "Il denominator gate trasferisce come supporto one-sided dell'ordine quando l'ordine e visibile agli osservabili canonici del perimetro, non come endpoint-stable support a due poli. Nel perimetro sintetico agent_20260507_0901, 4/4 domini non-BOUNDARY hanno endpoint_stable_observables=[] e polo coerente stable_count 3.0-5.0. Nel perimetro semi-reale agent_20260507_0923, primi e zeta trasferiscono (primi: SR,L1,triple_var; zeta: SR,L2), ma logistic_return_intervals e blank: stable_count coerente 0.0-0.2. Nel perimetro bridge agent_20260507_0942, prime_metric_delta_gamma_abs, prime_metric_dR_abs, zeta_trace_residual_step5_abs e hydrogen_bound_level_spacings trasferiscono su tutti i 5 osservabili canonici con endpoint_stable_observables=[]; e supporto perimetro-bridge, non universalita del gate. La beta 0.30/0.40/0.50 resta coordinata del protocollo quando compare, non coordinata universale.", tools/data/seme.json:54: "nota": "Input operatore 2026-04-10. Tocca: confine del modello, struttura relazionale dei punti fissi. Consecutio: quali punti fissi relazionali emergono dalle 21 tensioni attuali? Il grafo e gia nei dati?", tools/data/seme.json:65: "nota": "Input operatore 2026-04-10. Tocca: entropia come dispersione illusoria vs generazione dipolare. Consecutio: nel Lab i domini Poisson (entropia massima) mostrano dualita illusoria? I domini GUE (strutturati) mostrano dualita dipolare? Il drift verso Poisson (POISSON_CONVERGENCE) e perdita di dualita dipolare?", tools/data/seme.json:98: "nota": "Input operatore 2026-04-10. Tocca: ruolo di G nel tetraedro (T,Q,G,E). La fonte video_lp0RgZ6kQF8 dice: tensore metrico dentro la forma simplettica. G non e accanto a T,Q,E - e sotto. Consecutio: nei dati Lab, i ponti TxG e ExG hanno struttura diversa dai ponti TxQ?", tools/data/seme.json:119: "nota": "Input operatore 2026-04-10. Tocca: struttura locale degli assiomi. Consecutio: per ogni dominio Lab (primi, logistica, percolazione...) quali 2 assiomi del condensato sono operativi? Mappa assiomi x domini = grafo della realta locale.", tools/data/seme.json:140: "claim": "Applied valutatore REDESIGN from 20260507_0901: Falsificare ORDER_DENOMINATOR_GATE su domini non-sintetici o semi-reali: applicare il gate one-sided a 2-3 sequenze fisiche/ponte gia' presenti nel sito, con shuffle e surrogati preservanti marginale,", tools/data/seme.json:162: "Tensioni risolte: {'G_POTENZIALE_NULLA', 'DUALITA_DIPOLARE_VS_ILLUSORIA', 'PERTURBATION_DENOMINATOR_GATE', 'ORDER_DENOMINATOR_GATE', 'METRIC_TENSOR', 'TRAJECTORY_APPLY_20260507_0803', 'OBSERVABLE_REGISTRY', 'TENSIONE_ENTITA', 'TRAJECTORY_APPLY_20260507_0901', 'TRASCENDENZA_LIMITE', 'BOUNDARY_LAYER_GATE', 'PIANO_PRIMARIO_DUE_ASSIOMI'}" tools/data/seme.json:169: "direzione": "Falsificare ORDER_DENOMINATOR_GATE su domini non-sintetici o semi-reali: applicare il gate one-sided a 2-3 sequenze fisiche/ponte gia' presenti nel sito, con shuffle e surrogati preservanti marginale,", tools/data/reports/agent_20260507_0942.md:1:# Agent Report — Bridge Order Denominator Gate tools/data/reports/agent_20260507_0942.md:5:verdict: scoped_operator tools/data/reports/agent_20260507_0942.md:14:Verificato dal campo vivo: `ORDER_DENOMINATOR_GATE` e gia stato testato su tools/data/reports/agent_20260507_0942.md:68: `endpoint_stable_observables=[]` repeats across metric, trace, and QxE tools/data/reports/agent_20260507_0942.md:86:## Consecutio tools/data/reports/agent_20260507_0942.md:88:`ORDER_DENOMINATOR_GATE` survives the bridge falsification: tools/data/reports/agent_20260507_0942.md:103:L1 hard constraint vs bias: `endpoint_stable_observables=[]` is exact for the exec /bin/bash -lc 'python -m json.tool tools/data/bridge_order_denominator_gate_20260507_0942.json >/dev/null && python -m json.tool tools/data/bridge_order_denominator_gate_20260507_0942_seedcheck.json >/dev/null && python -m json.tool tools/data/seme.json >/dev/null' in /opt/MM_D-ND diff --git a/tools/data/reports/agent_20260507_0942.md b/tools/data/reports/agent_20260507_0942.md new file mode 100644 index 0000000000000000000000000000000000000000..da245bdaa717b348e2ecdd515e30dcb01065e2b4 --- /dev/null +++ b/tools/data/reports/agent_20260507_0942.md @@ -0,0 +1,131 @@ +# Agent Report — Bridge Order Denominator Gate + +timestamp: 2026-05-07 09:42 UTC +category: gate_falsification_bridge +verdict: scoped_operator +observables_registry: 1.0.0-2026-05-06 +observables_used: [SR, SR2, L1, L2, triple_var] +tool: tools/exp_bridge_order_denominator_gate.py +data: tools/data/bridge_order_denominator_gate_20260507_0942.json +seed_check: tools/data/bridge_order_denominator_gate_20260507_0942_seedcheck.json + +## Claim Under Test + +Verificato dal campo vivo: `ORDER_DENOMINATOR_GATE` e gia stato testato su +perimetri sintetici e semi-reali. Il mandato corrente chiede domini fisici o +ponte gia presenti nel sistema, con shuffle marginal-preserving. + +Domanda: se il perimetro e una sequenza bridge D-ND, il supporto denominatore +resta one-sided o compare un controesempio? + +Perimetri: + +- `prime_metric_delta_gamma_abs`: fluttuazioni della connessione metrica da + `g=(p/2)^2`. +- `prime_metric_dR_abs`: fluttuazioni di curvatura discreta `dR=R-2` dalla + stessa metrica. +- `zeta_trace_residual_step5_abs`: residuo non-lineare del trace bridge sugli + zeri di zeta al passo 5. +- `hydrogen_bound_level_spacings`: spacing dei livelli legati + `E_n=-1/(2n^2)`, ponte QxE. + +Gate: osservabile stabile se `abs(original - shuffle_mean) / shuffle_std >= 2`. + +## Deposito Numerico + +Run principale: `n_replicates=20`, `n_beta=11`, `n_baseline=32`, +`seed=202605070942`. Seed check: `n_replicates=12`, `n_baseline=24`, +`seed=202605070943`. + +| perimeter | coherent one-sided observables | stable_count coherent | stable_count illusory | endpoint distance gated | beta ambiguous gated | +|---|---:|---:|---:|---:|---:| +| prime_metric_delta_gamma_abs | SR, SR2, L1, L2, triple_var | 5.000 | 0.250 | 4.415 | [] | +| prime_metric_dR_abs | SR, SR2, L1, L2, triple_var | 5.000 | 0.350 | 4.414 | 0.30 | +| zeta_trace_residual_step5_abs | SR, SR2, L1, L2, triple_var | 5.000 | 0.050 | 4.415 | 0.30 | +| hydrogen_bound_level_spacings | SR, SR2, L1, L2, triple_var | 5.000 | 0.200 | 4.416 | 0.40 | + +Seed check: + +| perimeter | coherent one-sided observables | stable_count coherent | stable_count illusory | endpoint distance gated | beta ambiguous gated | +|---|---:|---:|---:|---:|---:| +| prime_metric_delta_gamma_abs | SR, SR2, L1, L2, triple_var | 5.000 | 0.167 | 4.377 | [] | +| prime_metric_dR_abs | SR, SR2, L1, L2, triple_var | 5.000 | 0.083 | 4.376 | [] | +| zeta_trace_residual_step5_abs | SR, SR2, L1, L2, triple_var | 5.000 | 0.083 | 4.377 | 0.30 | +| hydrogen_bound_level_spacings | SR, SR2, L1, L2, triple_var | 5.000 | 0.250 | 4.378 | 0.40, 0.50 | + +Endpoint-stable observables: `[]` in all four perimeters in both runs. + +## Risultato + +1. **The bridge perimeter carries full canonical one-sided support.** + + Each bridge sequence transfers on all five canonical observables. The + coherent endpoint has stable count `5.000` in both runs; the illusory endpoint + stays weak-denominator under the same marginal. + +2. **The both-endpoint support remains blank.** + + `endpoint_stable_observables=[]` repeats across metric, trace, and QxE + bridge perimeters. The gate is not two endpoint classes with stable + denominators. It is support on the side where order survives its shuffle null. + +3. **The logistic counter-scope from 09:23 still matters.** + + This result extends the gate into bridge sequences; it does not erase the + logistic return blank. The transferable object is order visible to the + canonical gap-observable contract. Return-time tail order still requires its + own observable perimetro. + +4. **The beta coordinate does not universalize.** + + Metric `delta_gamma` has no ambiguous gated beta in either run. Metric `dR` + has `0.30` in the main run and blank in seed check. Zeta trace keeps `0.30`. + Hydrogen gives `0.40` in the main run and `0.40, 0.50` in seed check. Beta is + protocol coordinate, not domain coordinate. + +## Consecutio + +`ORDER_DENOMINATOR_GATE` survives the bridge falsification: + +> In bridge perimeters whose order is carried by metric connection, +> curvature fluctuation, trace residual, or QxE level spacing sequences, the +> denominator gate transfers as full canonical one-sided support against a +> marginal-preserving shuffle. The logistic return perimeter remains the +> counter-scope for this observable contract. + +Next experiment: test the logistic counter-scope at the node regressivo: +symbolic itinerary block entropy, return-tail exponent, and recurrence-plot +diagonal statistics under the same original-vs-shuffle denominator gate. Do not +tune `z_min`. + +## Self-Audit: 5 Lenti + +L1 hard constraint vs bias: `endpoint_stable_observables=[]` is exact for the +declared perimeters and runs. The report does not claim universal absence of +illusory support; illusory stable counts are `0.050-0.350` in the main run. + +L2 quantity vs ratio: stable counts, endpoint distances, and z means are +reported before interpretation. No percent drift claim is used. + +L3 no silent patching: 09:23 found a logistic blank. This report does not patch +that blank; it adds bridge perimeters and keeps the counter-scope. + +L4 edge cases: hydrogen beta changes from `0.40` to `0.40,0.50` in seed check, +and metric `dR` beta drops from `0.30` to blank. The beta claim is explicitly +non-universal. + +L5 re-discovery vs discovery: metric, zeta, and hydrogen sequences sit near +known RMT/arithmetical/spectral order diagnostics. This report claims operator +scope inside the lab gate protocol, not a new theorem. + +## Fonti + +- Verificato: `tools/data/agent_field_live.md` +- Verificato: `tools/LAB_AGENT_CONTEXT.md` +- Verificato: `tools/observables_registry.py` +- Verificato: `tools/exp_bridge_order_denominator_gate.py` +- Verificato: `tools/data/bridge_order_denominator_gate_20260507_0942.json` +- Verificato: `tools/data/bridge_order_denominator_gate_20260507_0942_seedcheck.json` +- Inferito: bridge perimeters carry full canonical one-sided support because + all five observables pass the original-vs-shuffle gate at the coherent + endpoint and fail endpoint-stable support in both runs. diff --git a/tools/data/seme.json b/tools/data/seme.json index 0c6e20e89b979c05b2f3d06089e734d017a0159d..a081e04934a85172c6941c0e2f636bbace866214 --- a/tools/data/seme.json +++ b/tools/data/seme.json @@ -38,12 +38,12 @@ { "tipo": "vincolo", "id": "ORDER_DENOMINATOR_GATE", - "claim": "Il denominator gate trasferisce come supporto one-sided dell'ordine quando l'ordine e visibile agli osservabili canonici del perimetro, non come endpoint-stable support a due poli. Nel perimetro sintetico agent_20260507_0901, 4/4 domini non-BOUNDARY hanno endpoint_stable_observables=[] e polo coerente stable_count 3.0-5.0. Nel perimetro semi-reale agent_20260507_0923, primi e zeta trasferiscono (primi: SR,L1,triple_var; zeta: SR,L2), ma logistic_return_intervals e blank: stable_count coerente 0.0-0.2. La beta 0.30 e coordinata del protocollo quando compare, non coordinata universale.", + "claim": "Il denominator gate trasferisce come supporto one-sided dell'ordine quando l'ordine e visibile agli osservabili canonici del perimetro, non come endpoint-stable support a due poli. Nel perimetro sintetico agent_20260507_0901, 4/4 domini non-BOUNDARY hanno endpoint_stable_observables=[] e polo coerente stable_count 3.0-5.0. Nel perimetro semi-reale agent_20260507_0923, primi e zeta trasferiscono (primi: SR,L1,triple_var; zeta: SR,L2), ma logistic_return_intervals e blank: stable_count coerente 0.0-0.2. Nel perimetro bridge agent_20260507_0942, prime_metric_delta_gamma_abs, prime_metric_dR_abs, zeta_trace_residual_step5_abs e hydrogen_bound_level_spacings trasferiscono su tutti i 5 osservabili canonici con endpoint_stable_observables=[]; e supporto perimetro-bridge, non universalita del gate. La beta 0.30/0.40/0.50 resta coordinata del protocollo quando compare, non coordinata universale.", "intensita": 0.92, "porta": "META", "manuale": true, "condensato_ref": "A4,A8,A14,C2", - "origine": "cycle agent_20260507_0901 + agent_20260507_0923: transfer matrix sintetica e falsificazione semi-reale su primi, zeta, logistic returns", + "origine": "cycle agent_20260507_0901 + agent_20260507_0923 + agent_20260507_0942: transfer matrix sintetica, falsificazione semi-reale su primi/zeta/logistic returns, e perimetri bridge metric/trace/QxE", "added_at": "2026-05-07T09:01:00+00:00" }, { @@ -333,4 +333,4 @@ "timestamp": "2026-04-04T19:34:08.014Z" } ] -} \ No newline at end of file +} diff --git a/tools/exp_bridge_order_denominator_gate.py b/tools/exp_bridge_order_denominator_gate.py new file mode 100644 index 0000000000000000000000000000000000000000..17286d6199454f089722ec1e0e6bad01f25f3b92 --- /dev/null +++ b/tools/exp_bridge_order_denominator_gate.py @@ -0,0 +1,206 @@ +#!/usr/bin/env python3 +""" +exp_bridge_order_denominator_gate.py + +Falsification attempt for ORDER_DENOMINATOR_GATE on bridge/perimeter sequences +already present in the D-ND lab context: + +- prime metric connection fluctuations from g=(p/2)^2 +- prime metric curvature fluctuations dR +- zeta trace-bridge nonlinear residuals +- hydrogen bound-level spacings from the QxE bridge + +The coherent endpoint is the observed/generated bridge order. The illusory +endpoint is a marginal-preserving permutation. Canonical observables come from +observables_registry.py. +""" + +from __future__ import annotations + +import argparse +import json +import math +from pathlib import Path + +import numpy as np + +from exp_semireal_order_denominator_gate import analyze_sequence, compact, normalize +from observables_registry import OBSERVABLES_REGISTRY_VERSION, OBSERVABLES_CANONICAL + + +OBS_NAMES = list(OBSERVABLES_CANONICAL.keys()) +PHI = (1.0 + math.sqrt(5.0)) / 2.0 +LAMBDA = -1.0 / PHI**2 +DATA_DIR = Path(__file__).parent / "data" + + +def sieve_primes_for_count(n_primes: int) -> np.ndarray: + if n_primes < 6: + limit = 20 + else: + limit = int(n_primes * (math.log(n_primes) + math.log(math.log(n_primes))) * 1.35) + while True: + sieve = np.ones(limit + 1, dtype=bool) + sieve[:2] = False + for p in range(2, int(limit**0.5) + 1): + if sieve[p]: + sieve[p * p : limit + 1 : p] = False + primes = np.flatnonzero(sieve) + if len(primes) >= n_primes: + return primes[:n_primes].astype(float) + limit *= 2 + + +def positive_bridge_values(values: np.ndarray) -> np.ndarray: + """Map a signed bridge observable to positive values without changing order.""" + values = np.asarray(values, dtype=float) + values = values[np.isfinite(values)] + values = np.abs(values) + return normalize(values + 1e-12) + + +def prime_metric_delta_gamma(n_values: int) -> np.ndarray: + primes = sieve_primes_for_count(n_values + 3) + p = primes.astype(float) + tau = np.log(p) + metric = (p / 2.0) ** 2 + dg = np.diff(metric) + dtau = np.diff(tau) + mid = (metric[:-1] + metric[1:]) / 2.0 + gamma = dg / (2.0 * mid * dtau) + delta_gamma = np.diff(gamma) + return positive_bridge_values(delta_gamma[:n_values]) + + +def prime_metric_dR(n_values: int) -> np.ndarray: + primes = sieve_primes_for_count(n_values + 3) + seq = primes.astype(float) + t = np.log(seq) + a = seq / 2.0 + dt = np.diff(t) + dt_mid = (dt[:-1] + dt[1:]) / 2.0 + da = np.diff(a) + a_prime = da / dt + da_prime = np.diff(a_prime) + a_double_prime = da_prime / dt_mid + r_n = 2.0 * a_double_prime / a[1:-1] + d_r = r_n - 2.0 + return positive_bridge_values(d_r[:n_values]) + + +def load_zeta_zeros(n_zeros: int) -> np.ndarray: + zeros_file = DATA_DIR / "odlyzko_cache" / "zeros1.txt" + if not zeros_file.exists(): + raise RuntimeError(f"{zeros_file} not found") + zeros: list[float] = [] + with zeros_file.open() as f: + for line in f: + line = line.strip() + if not line: + continue + zeros.append(float(line)) + if len(zeros) >= n_zeros: + break + if len(zeros) < n_zeros: + raise RuntimeError(f"only {len(zeros)} zeta zeros available, need {n_zeros}") + return np.array(zeros, dtype=float) + + +def dnd_map_trajectory(x0: float, n_iter: int) -> np.ndarray: + x = float(x0) + traj = [x] + for _ in range(n_iter): + if abs(x) < 1e-15: + break + x = 1.0 + 1.0 / x + if not np.isfinite(x): + break + traj.append(x) + return np.array(traj, dtype=float) + + +def zeta_trace_residual(n_values: int, step: int = 5) -> np.ndarray: + zeros = load_zeta_zeros(n_values) + residuals = [] + for x0 in zeros: + traj = dnd_map_trajectory(float(x0), max(step + 2, 15)) + if len(traj) <= step: + continue + linear = PHI + (float(x0) - PHI) * (LAMBDA**step) + residuals.append(traj[step] - linear) + return positive_bridge_values(np.array(residuals[:n_values], dtype=float)) + + +def hydrogen_bound_level_spacings(n_values: int) -> np.ndarray: + # Atomic units: E_n = -1/(2n^2). Positive adjacent spacings shrink smoothly. + n = np.arange(1, n_values + 2, dtype=float) + energy = -1.0 / (2.0 * n**2) + spacings = np.diff(energy) + return normalize(spacings) + + +def build_sequences(args: argparse.Namespace) -> dict[str, np.ndarray]: + return { + "prime_metric_delta_gamma_abs": prime_metric_delta_gamma(args.n_gaps), + "prime_metric_dR_abs": prime_metric_dR(args.n_gaps), + "zeta_trace_residual_step5_abs": zeta_trace_residual(args.zeta_values, step=5), + "hydrogen_bound_level_spacings": hydrogen_bound_level_spacings(args.n_gaps), + } + + +def run(args: argparse.Namespace) -> dict: + root_rng = np.random.default_rng(args.seed) + sequences = build_sequences(args) + perimeters = {} + for name, base in sequences.items(): + perimeters[name] = analyze_sequence(name, base, args, root_rng) + + output = { + "experiment": "bridge_order_denominator_gate", + "category": "gate_falsification_bridge", + "question": "Does ORDER_DENOMINATOR_GATE survive on D-ND bridge sequences beyond prime/zeta/logistic gaps?", + "observables_registry": OBSERVABLES_REGISTRY_VERSION, + "observables_used": OBS_NAMES, + "params": vars(args), + "matrix": compact(perimeters), + "perimeters": perimeters, + } + + out_path = Path(args.out) + out_path.parent.mkdir(parents=True, exist_ok=True) + with out_path.open("w") as f: + json.dump(output, f, indent=2) + + print(f"observables_registry={OBSERVABLES_REGISTRY_VERSION}") + print(f"observables_used={OBS_NAMES}") + print("perimeter n one_sided stable0 stable1 dist_gate ambiguous_gate") + for name, row in output["matrix"].items(): + print( + f"{name:>34s} " + f"{row['n_gaps']:>5d} " + f"{','.join(row['coherent_one_sided_observables']) or '[]':>22s} " + f"{row['stable_count_coherent']:>7.3f} " + f"{row['stable_count_illusory']:>7.3f} " + f"{row['endpoint_distance_one_sided_gated']:>9.3f} " + f"{row['ambiguous_beta_one_sided_gated']}" + ) + print(f"saved {out_path}") + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--n-gaps", type=int, default=4096) + parser.add_argument("--zeta-values", type=int, default=2000) + parser.add_argument("--n-replicates", type=int, default=20) + parser.add_argument("--n-beta", type=int, default=11) + parser.add_argument("--n-baseline", type=int, default=32) + parser.add_argument("--z-min", type=float, default=2.0) + parser.add_argument("--seed", type=int, default=202605070942) + parser.add_argument("--out", default="tools/data/bridge_order_denominator_gate_20260507_0942.json") + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() succeeded in 184ms: codex Eseguito il cycle e scritto il report richiesto: [tools/data/reports/agent_20260507_0942.md](/opt/MM_D-ND/tools/data/reports/agent_20260507_0942.md) Ho aggiunto anche lo strumento riusabile: [tools/exp_bridge_order_denominator_gate.py](/opt/MM_D-ND/tools/exp_bridge_order_denominator_gate.py) Output dati: [bridge_order_denominator_gate_20260507_0942.json](/opt/MM_D-ND/tools/data/bridge_order_denominator_gate_20260507_0942.json) [bridge_order_denominator_gate_20260507_0942_seedcheck.json](/opt/MM_D-ND/tools/data/bridge_order_denominator_gate_20260507_0942_seedcheck.json) Risultato sintetico: `ORDER_DENOMINATOR_GATE` trasferisce sui quattro perimetri bridge testati come supporto one-sided su tutti i 5 osservabili canonici, con `endpoint_stable_observables=[]`. Il report mantiene il counter-scope logistic del cycle 09:23 e non universalizza la beta. Verifiche fatte: `py_compile` dello script e validazione JSON di output + `tools/data/seme.json`. Ho aggiornato `tools/data/seme.json` nel claim `ORDER_DENOMINATOR_GATE` con il nuovo perimetro 09:42. diff --git a/tools/data/reports/agent_20260507_0942.md b/tools/data/reports/agent_20260507_0942.md new file mode 100644 index 0000000000000000000000000000000000000000..da245bdaa717b348e2ecdd515e30dcb01065e2b4 --- /dev/null +++ b/tools/data/reports/agent_20260507_0942.md @@ -0,0 +1,131 @@ +# Agent Report — Bridge Order Denominator Gate + +timestamp: 2026-05-07 09:42 UTC +category: gate_falsification_bridge +verdict: scoped_operator +observables_registry: 1.0.0-2026-05-06 +observables_used: [SR, SR2, L1, L2, triple_var] +tool: tools/exp_bridge_order_denominator_gate.py +data: tools/data/bridge_order_denominator_gate_20260507_0942.json +seed_check: tools/data/bridge_order_denominator_gate_20260507_0942_seedcheck.json + +## Claim Under Test + +Verificato dal campo vivo: `ORDER_DENOMINATOR_GATE` e gia stato testato su +perimetri sintetici e semi-reali. Il mandato corrente chiede domini fisici o +ponte gia presenti nel sistema, con shuffle marginal-preserving. + +Domanda: se il perimetro e una sequenza bridge D-ND, il supporto denominatore +resta one-sided o compare un controesempio? + +Perimetri: + +- `prime_metric_delta_gamma_abs`: fluttuazioni della connessione metrica da + `g=(p/2)^2`. +- `prime_metric_dR_abs`: fluttuazioni di curvatura discreta `dR=R-2` dalla + stessa metrica. +- `zeta_trace_residual_step5_abs`: residuo non-lineare del trace bridge sugli + zeri di zeta al passo 5. +- `hydrogen_bound_level_spacings`: spacing dei livelli legati + `E_n=-1/(2n^2)`, ponte QxE. + +Gate: osservabile stabile se `abs(original - shuffle_mean) / shuffle_std >= 2`. + +## Deposito Numerico + +Run principale: `n_replicates=20`, `n_beta=11`, `n_baseline=32`, +`seed=202605070942`. Seed check: `n_replicates=12`, `n_baseline=24`, +`seed=202605070943`. + +| perimeter | coherent one-sided observables | stable_count coherent | stable_count illusory | endpoint distance gated | beta ambiguous gated | +|---|---:|---:|---:|---:|---:| +| prime_metric_delta_gamma_abs | SR, SR2, L1, L2, triple_var | 5.000 | 0.250 | 4.415 | [] | +| prime_metric_dR_abs | SR, SR2, L1, L2, triple_var | 5.000 | 0.350 | 4.414 | 0.30 | +| zeta_trace_residual_step5_abs | SR, SR2, L1, L2, triple_var | 5.000 | 0.050 | 4.415 | 0.30 | +| hydrogen_bound_level_spacings | SR, SR2, L1, L2, triple_var | 5.000 | 0.200 | 4.416 | 0.40 | + +Seed check: + +| perimeter | coherent one-sided observables | stable_count coherent | stable_count illusory | endpoint distance gated | beta ambiguous gated | +|---|---:|---:|---:|---:|---:| +| prime_metric_delta_gamma_abs | SR, SR2, L1, L2, triple_var | 5.000 | 0.167 | 4.377 | [] | +| prime_metric_dR_abs | SR, SR2, L1, L2, triple_var | 5.000 | 0.083 | 4.376 | [] | +| zeta_trace_residual_step5_abs | SR, SR2, L1, L2, triple_var | 5.000 | 0.083 | 4.377 | 0.30 | +| hydrogen_bound_level_spacings | SR, SR2, L1, L2, triple_var | 5.000 | 0.250 | 4.378 | 0.40, 0.50 | + +Endpoint-stable observables: `[]` in all four perimeters in both runs. + +## Risultato + +1. **The bridge perimeter carries full canonical one-sided support.** + + Each bridge sequence transfers on all five canonical observables. The + coherent endpoint has stable count `5.000` in both runs; the illusory endpoint + stays weak-denominator under the same marginal. + +2. **The both-endpoint support remains blank.** + + `endpoint_stable_observables=[]` repeats across metric, trace, and QxE + bridge perimeters. The gate is not two endpoint classes with stable + denominators. It is support on the side where order survives its shuffle null. + +3. **The logistic counter-scope from 09:23 still matters.** + + This result extends the gate into bridge sequences; it does not erase the + logistic return blank. The transferable object is order visible to the + canonical gap-observable contract. Return-time tail order still requires its + own observable perimetro. + +4. **The beta coordinate does not universalize.** + + Metric `delta_gamma` has no ambiguous gated beta in either run. Metric `dR` + has `0.30` in the main run and blank in seed check. Zeta trace keeps `0.30`. + Hydrogen gives `0.40` in the main run and `0.40, 0.50` in seed check. Beta is + protocol coordinate, not domain coordinate. + +## Consecutio + +`ORDER_DENOMINATOR_GATE` survives the bridge falsification: + +> In bridge perimeters whose order is carried by metric connection, +> curvature fluctuation, trace residual, or QxE level spacing sequences, the +> denominator gate transfers as full canonical one-sided support against a +> marginal-preserving shuffle. The logistic return perimeter remains the +> counter-scope for this observable contract. + +Next experiment: test the logistic counter-scope at the node regressivo: +symbolic itinerary block entropy, return-tail exponent, and recurrence-plot +diagonal statistics under the same original-vs-shuffle denominator gate. Do not +tune `z_min`. + +## Self-Audit: 5 Lenti + +L1 hard constraint vs bias: `endpoint_stable_observables=[]` is exact for the +declared perimeters and runs. The report does not claim universal absence of +illusory support; illusory stable counts are `0.050-0.350` in the main run. + +L2 quantity vs ratio: stable counts, endpoint distances, and z means are +reported before interpretation. No percent drift claim is used. + +L3 no silent patching: 09:23 found a logistic blank. This report does not patch +that blank; it adds bridge perimeters and keeps the counter-scope. + +L4 edge cases: hydrogen beta changes from `0.40` to `0.40,0.50` in seed check, +and metric `dR` beta drops from `0.30` to blank. The beta claim is explicitly +non-universal. + +L5 re-discovery vs discovery: metric, zeta, and hydrogen sequences sit near +known RMT/arithmetical/spectral order diagnostics. This report claims operator +scope inside the lab gate protocol, not a new theorem. + +## Fonti + +- Verificato: `tools/data/agent_field_live.md` +- Verificato: `tools/LAB_AGENT_CONTEXT.md` +- Verificato: `tools/observables_registry.py` +- Verificato: `tools/exp_bridge_order_denominator_gate.py` +- Verificato: `tools/data/bridge_order_denominator_gate_20260507_0942.json` +- Verificato: `tools/data/bridge_order_denominator_gate_20260507_0942_seedcheck.json` +- Inferito: bridge perimeters carry full canonical one-sided support because + all five observables pass the original-vs-shuffle gate at the coherent + endpoint and fail endpoint-stable support in both runs. diff --git a/tools/data/seme.json b/tools/data/seme.json index 0c6e20e89b979c05b2f3d06089e734d017a0159d..a081e04934a85172c6941c0e2f636bbace866214 --- a/tools/data/seme.json +++ b/tools/data/seme.json @@ -38,12 +38,12 @@ { "tipo": "vincolo", "id": "ORDER_DENOMINATOR_GATE", - "claim": "Il denominator gate trasferisce come supporto one-sided dell'ordine quando l'ordine e visibile agli osservabili canonici del perimetro, non come endpoint-stable support a due poli. Nel perimetro sintetico agent_20260507_0901, 4/4 domini non-BOUNDARY hanno endpoint_stable_observables=[] e polo coerente stable_count 3.0-5.0. Nel perimetro semi-reale agent_20260507_0923, primi e zeta trasferiscono (primi: SR,L1,triple_var; zeta: SR,L2), ma logistic_return_intervals e blank: stable_count coerente 0.0-0.2. La beta 0.30 e coordinata del protocollo quando compare, non coordinata universale.", + "claim": "Il denominator gate trasferisce come supporto one-sided dell'ordine quando l'ordine e visibile agli osservabili canonici del perimetro, non come endpoint-stable support a due poli. Nel perimetro sintetico agent_20260507_0901, 4/4 domini non-BOUNDARY hanno endpoint_stable_observables=[] e polo coerente stable_count 3.0-5.0. Nel perimetro semi-reale agent_20260507_0923, primi e zeta trasferiscono (primi: SR,L1,triple_var; zeta: SR,L2), ma logistic_return_intervals e blank: stable_count coerente 0.0-0.2. Nel perimetro bridge agent_20260507_0942, prime_metric_delta_gamma_abs, prime_metric_dR_abs, zeta_trace_residual_step5_abs e hydrogen_bound_level_spacings trasferiscono su tutti i 5 osservabili canonici con endpoint_stable_observables=[]; e supporto perimetro-bridge, non universalita del gate. La beta 0.30/0.40/0.50 resta coordinata del protocollo quando compare, non coordinata universale.", "intensita": 0.92, "porta": "META", "manuale": true, "condensato_ref": "A4,A8,A14,C2", - "origine": "cycle agent_20260507_0901 + agent_20260507_0923: transfer matrix sintetica e falsificazione semi-reale su primi, zeta, logistic returns", + "origine": "cycle agent_20260507_0901 + agent_20260507_0923 + agent_20260507_0942: transfer matrix sintetica, falsificazione semi-reale su primi/zeta/logistic returns, e perimetri bridge metric/trace/QxE", "added_at": "2026-05-07T09:01:00+00:00" }, { @@ -333,4 +333,4 @@ "timestamp": "2026-04-04T19:34:08.014Z" } ] -} \ No newline at end of file +} diff --git a/tools/exp_bridge_order_denominator_gate.py b/tools/exp_bridge_order_denominator_gate.py new file mode 100644 index 0000000000000000000000000000000000000000..17286d6199454f089722ec1e0e6bad01f25f3b92 --- /dev/null +++ b/tools/exp_bridge_order_denominator_gate.py @@ -0,0 +1,206 @@ +#!/usr/bin/env python3 +""" +exp_bridge_order_denominator_gate.py + +Falsification attempt for ORDER_DENOMINATOR_GATE on bridge/perimeter sequences +already present in the D-ND lab context: + +- prime metric connection fluctuations from g=(p/2)^2 +- prime metric curvature fluctuations dR +- zeta trace-bridge nonlinear residuals +- hydrogen bound-level spacings from the QxE bridge + +The coherent endpoint is the observed/generated bridge order. The illusory +endpoint is a marginal-preserving permutation. Canonical observables come from +observables_registry.py. +""" + +from __future__ import annotations + +import argparse +import json +import math +from pathlib import Path + +import numpy as np + +from exp_semireal_order_denominator_gate import analyze_sequence, compact, normalize +from observables_registry import OBSERVABLES_REGISTRY_VERSION, OBSERVABLES_CANONICAL + + +OBS_NAMES = list(OBSERVABLES_CANONICAL.keys()) +PHI = (1.0 + math.sqrt(5.0)) / 2.0 +LAMBDA = -1.0 / PHI**2 +DATA_DIR = Path(__file__).parent / "data" + + +def sieve_primes_for_count(n_primes: int) -> np.ndarray: + if n_primes < 6: + limit = 20 + else: + limit = int(n_primes * (math.log(n_primes) + math.log(math.log(n_primes))) * 1.35) + while True: + sieve = np.ones(limit + 1, dtype=bool) + sieve[:2] = False + for p in range(2, int(limit**0.5) + 1): + if sieve[p]: + sieve[p * p : limit + 1 : p] = False + primes = np.flatnonzero(sieve) + if len(primes) >= n_primes: + return primes[:n_primes].astype(float) + limit *= 2 + + +def positive_bridge_values(values: np.ndarray) -> np.ndarray: + """Map a signed bridge observable to positive values without changing order.""" + values = np.asarray(values, dtype=float) + values = values[np.isfinite(values)] + values = np.abs(values) + return normalize(values + 1e-12) + + +def prime_metric_delta_gamma(n_values: int) -> np.ndarray: + primes = sieve_primes_for_count(n_values + 3) + p = primes.astype(float) + tau = np.log(p) + metric = (p / 2.0) ** 2 + dg = np.diff(metric) + dtau = np.diff(tau) + mid = (metric[:-1] + metric[1:]) / 2.0 + gamma = dg / (2.0 * mid * dtau) + delta_gamma = np.diff(gamma) + return positive_bridge_values(delta_gamma[:n_values]) + + +def prime_metric_dR(n_values: int) -> np.ndarray: + primes = sieve_primes_for_count(n_values + 3) + seq = primes.astype(float) + t = np.log(seq) + a = seq / 2.0 + dt = np.diff(t) + dt_mid = (dt[:-1] + dt[1:]) / 2.0 + da = np.diff(a) + a_prime = da / dt + da_prime = np.diff(a_prime) + a_double_prime = da_prime / dt_mid + r_n = 2.0 * a_double_prime / a[1:-1] + d_r = r_n - 2.0 + return positive_bridge_values(d_r[:n_values]) + + +def load_zeta_zeros(n_zeros: int) -> np.ndarray: + zeros_file = DATA_DIR / "odlyzko_cache" / "zeros1.txt" + if not zeros_file.exists(): + raise RuntimeError(f"{zeros_file} not found") + zeros: list[float] = [] + with zeros_file.open() as f: + for line in f: + line = line.strip() + if not line: + continue + zeros.append(float(line)) + if len(zeros) >= n_zeros: + break + if len(zeros) < n_zeros: + raise RuntimeError(f"only {len(zeros)} zeta zeros available, need {n_zeros}") + return np.array(zeros, dtype=float) + + +def dnd_map_trajectory(x0: float, n_iter: int) -> np.ndarray: + x = float(x0) + traj = [x] + for _ in range(n_iter): + if abs(x) < 1e-15: + break + x = 1.0 + 1.0 / x + if not np.isfinite(x): + break + traj.append(x) + return np.array(traj, dtype=float) + + +def zeta_trace_residual(n_values: int, step: int = 5) -> np.ndarray: + zeros = load_zeta_zeros(n_values) + residuals = [] + for x0 in zeros: + traj = dnd_map_trajectory(float(x0), max(step + 2, 15)) + if len(traj) <= step: + continue + linear = PHI + (float(x0) - PHI) * (LAMBDA**step) + residuals.append(traj[step] - linear) + return positive_bridge_values(np.array(residuals[:n_values], dtype=float)) + + +def hydrogen_bound_level_spacings(n_values: int) -> np.ndarray: + # Atomic units: E_n = -1/(2n^2). Positive adjacent spacings shrink smoothly. + n = np.arange(1, n_values + 2, dtype=float) + energy = -1.0 / (2.0 * n**2) + spacings = np.diff(energy) + return normalize(spacings) + + +def build_sequences(args: argparse.Namespace) -> dict[str, np.ndarray]: + return { + "prime_metric_delta_gamma_abs": prime_metric_delta_gamma(args.n_gaps), + "prime_metric_dR_abs": prime_metric_dR(args.n_gaps), + "zeta_trace_residual_step5_abs": zeta_trace_residual(args.zeta_values, step=5), + "hydrogen_bound_level_spacings": hydrogen_bound_level_spacings(args.n_gaps), + } + + +def run(args: argparse.Namespace) -> dict: + root_rng = np.random.default_rng(args.seed) + sequences = build_sequences(args) + perimeters = {} + for name, base in sequences.items(): + perimeters[name] = analyze_sequence(name, base, args, root_rng) + + output = { + "experiment": "bridge_order_denominator_gate", + "category": "gate_falsification_bridge", + "question": "Does ORDER_DENOMINATOR_GATE survive on D-ND bridge sequences beyond prime/zeta/logistic gaps?", + "observables_registry": OBSERVABLES_REGISTRY_VERSION, + "observables_used": OBS_NAMES, + "params": vars(args), + "matrix": compact(perimeters), + "perimeters": perimeters, + } + + out_path = Path(args.out) + out_path.parent.mkdir(parents=True, exist_ok=True) + with out_path.open("w") as f: + json.dump(output, f, indent=2) + + print(f"observables_registry={OBSERVABLES_REGISTRY_VERSION}") + print(f"observables_used={OBS_NAMES}") + print("perimeter n one_sided stable0 stable1 dist_gate ambiguous_gate") + for name, row in output["matrix"].items(): + print( + f"{name:>34s} " + f"{row['n_gaps']:>5d} " + f"{','.join(row['coherent_one_sided_observables']) or '[]':>22s} " + f"{row['stable_count_coherent']:>7.3f} " + f"{row['stable_count_illusory']:>7.3f} " + f"{row['endpoint_distance_one_sided_gated']:>9.3f} " + f"{row['ambiguous_beta_one_sided_gated']}" + ) + print(f"saved {out_path}") + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--n-gaps", type=int, default=4096) + parser.add_argument("--zeta-values", type=int, default=2000) + parser.add_argument("--n-replicates", type=int, default=20) + parser.add_argument("--n-beta", type=int, default=11) + parser.add_argument("--n-baseline", type=int, default=32) + parser.add_argument("--z-min", type=float, default=2.0) + parser.add_argument("--seed", type=int, default=202605070942) + parser.add_argument("--out", default="tools/data/bridge_order_denominator_gate_20260507_0942.json") + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() tokens used 92,965 Eseguito il cycle e scritto il report richiesto: [tools/data/reports/agent_20260507_0942.md](/opt/MM_D-ND/tools/data/reports/agent_20260507_0942.md) Ho aggiunto anche lo strumento riusabile: [tools/exp_bridge_order_denominator_gate.py](/opt/MM_D-ND/tools/exp_bridge_order_denominator_gate.py) Output dati: [bridge_order_denominator_gate_20260507_0942.json](/opt/MM_D-ND/tools/data/bridge_order_denominator_gate_20260507_0942.json) [bridge_order_denominator_gate_20260507_0942_seedcheck.json](/opt/MM_D-ND/tools/data/bridge_order_denominator_gate_20260507_0942_seedcheck.json) Risultato sintetico: `ORDER_DENOMINATOR_GATE` trasferisce sui quattro perimetri bridge testati come supporto one-sided su tutti i 5 osservabili canonici, con `endpoint_stable_observables=[]`. Il report mantiene il counter-scope logistic del cycle 09:23 e non universalizza la beta. Verifiche fatte: `py_compile` dello script e validazione JSON di output + `tools/data/seme.json`. Ho aggiornato `tools/data/seme.json` nel claim `ORDER_DENOMINATOR_GATE` con il nuovo perimetro 09:42.